Complete SR007 profile reconciliation and qualify historical revenue categories

Reviewed-on: #43

apps/docs/scripts/auth-cli.ts

@@ -182,10 +182,10 @@ async function main() {
   assert.equal(researchResponse.status, 200, "authenticated research API");
   assert.ok(researchResponse.headers.get("cache-control")?.includes("no-store"));
   const research = await researchResponse.json();
-  assert.equal(research.companies.length, 1023);
+  assert.equal(research.companies.length, 1253);
   assert.equal(research.categories.length, 11);
-  assert.equal(research.evidence.length, 1318);
-  assert.equal(research.observations.length, 189);
+  assert.equal(research.evidence.length, 4894);
+  assert.equal(research.observations.length, 491);
   assert.equal((await request("/api/research/ai-companies", { method: "POST", headers })).status, 405);
   const researchPage = await request("/docs/other-research/ai-company-research/overview", { headers });
   assert.equal(researchPage.status, 200);

apps/docs/src/components/AICompanyResearch.tsx

@@ -1,15 +1,18 @@
 import { useEffect, useMemo, useRef, useState } from "react";
 import { useRouter } from "next/router";
 import IncomeDistribution from "./IncomeDistribution";
+import ArrDisclosures from "./ArrDisclosures";
+import BusinessModelAssessment from "./BusinessModelAssessment";
+import ResearchCandidates from "./ResearchCandidates";
 import {
   categorySummary, evidenceUrl, technicalReviewLabels, reviewCompanyIds, type ResearchEvidence, type ResearchReview, type ResearchSnapshot,
 } from "@/lib/ai-research";
 
 const pageSize = 12;
 const evidencePageSize = 30;
-const revenueLabels = { self: "公司披露，待核口径", estimate: "仅第三方估算", platform: "仅支付平台记录", unknown: "暂无可用收入数字" };
+const revenueLabels = { self: "企业披露，待核口径", estimate: "仅第三方估算", platform: "仅支付平台记录", unknown: "暂无可用收入数字" };
 const useLabels: Record<string, string> = {
-  platform: "支付平台记录", signal: "公司披露信号", estimate: "第三方估算", historical: "历史里程碑",
+  platform: "支付平台记录", signal: "企业披露信号", estimate: "第三方估算", historical: "历史里程碑",
   "historical-estimate": "其他时点估算", excluded: "排除收入比较", scale: "团队规模", adoption: "采用规模",
   funding: "融资", technical: "第三方技术评测", crosscheck: "同一里程碑的其他资料",
 };
@@ -55,7 +58,7 @@ export default function AICompanyResearch() {
     : router.isReady && typeof linkedCompany === "string" && data?.companies.some((c) => c.id === linkedCompany)
       ? linkedCompany : null;
   function setSelected(id: string | null) { setSelection({ route: router.asPath, id }); }
-  const [tab, setTab] = useState<"companies" | "revenue" | "sources" | "reviews">("companies");
+  const [tab, setTab] = useState<"companies" | "candidates" | "revenue" | "models" | "sources" | "reviews">("companies");
   const companySection = useRef<HTMLElement>(null);
   const detailSection = useRef<HTMLElement>(null);
   useEffect(() => {
@@ -127,7 +130,7 @@ export default function AICompanyResearch() {
     <section ref={companySection} className="research-section" aria-labelledby="research-records-title">
       <h2 id="research-records-title">研究数据</h2>
       <div className="research-tabs" role="group" aria-label="研究数据视图">
-        {([['companies','公司与产品'],['revenue','收入观测'],['sources','原始证据'],['reviews','核查记录']] as const).map(([id,label]) =>
+        {([['companies','公司与产品'],['candidates','候选与缺项'],['revenue','收入观测'],['models','商业模式初评'],['sources','原始证据'],['reviews','核查记录']] as const).map(([id,label]) =>
           <button key={id} aria-pressed={tab === id} onClick={() => setTab(id)}>{label}</button>)}
       </div>
       {tab === "companies" && <>
@@ -138,7 +141,7 @@ export default function AICompanyResearch() {
           <label>资料深度<select value={depth} onChange={(e) => { setDepth(e.target.value); setPage(0); }}><option value="">全部资料</option><option value="case">原有个案研究</option><option value="profile">目录基础资料</option></select></label>
           <label>记录团队规模<select value={teamBand} onChange={(e) => { setTeamBand(e.target.value); setPage(0); }}><option value="">全部规模</option><option value="small">1—5 人</option><option value="medium">6—20 人</option><option value="large">21—50 人</option><option value="xl">至少 51 人</option><option value="unknown">未披露或分组不确定</option></select></label>
           <label>技术验证证据<select value={technicalLevel} onChange={(e) => { setTechnicalLevel(e.target.value); setPage(0); }}><option value="">全部验证层级</option>{Object.entries(technicalReviewLabels).map(([value, label]) => <option key={value} value={value}>{label}</option>)}</select></label>
-          <label>收入资料<select value={income} onChange={(e) => { setIncome(e.target.value); setPage(0); }}><option value="">全部状态</option><option value="self">找到公司披露</option><option value="estimate">仅第三方估算</option><option value="platform">仅支付平台记录</option><option value="unknown">暂无可用数字</option></select></label>
+          <label>收入资料<select value={income} onChange={(e) => { setIncome(e.target.value); setPage(0); }}><option value="">全部状态</option><option value="self">找到企业披露</option><option value="estimate">仅第三方估算</option><option value="platform">仅支付平台记录</option><option value="unknown">暂无可用数字</option></select></label>
         </div>
         <p className="research-muted">技术验证层级记录我们查到了什么，不是能力强弱评分。技术专项评审与业务分类复核分别记录。</p>
         <div className="research-result-count" role="status">{filtered.length} 条匹配记录<button onClick={() => { setCategory(""); setQuery(""); setCohort(""); setIncome(""); setDepth(""); setTeamBand(""); setTechnicalLevel(""); setPage(0); }}>清除筛选</button></div>
@@ -152,16 +155,19 @@ export default function AICompanyResearch() {
         {!filtered.length && <p className="research-message">没有匹配公司，请调整筛选条件。</p>}
         {filtered.length > pageSize && <nav className="research-pagination" aria-label="公司数据分页"><button disabled={page === 0} onClick={() => setPage((n) => n - 1)}>上一页</button><span>{page + 1} / {Math.ceil(filtered.length / pageSize)}</span><button disabled={(page + 1) * pageSize >= filtered.length} onClick={() => setPage((n) => n + 1)}>下一页</button></nav>}
       </>}
+      {tab === "models" && <BusinessModelAssessment data={data} onSelect={setSelected} onChooseCategory={chooseCategory} />}
+      {tab === "candidates" && <ResearchCandidates data={data} onSelect={setSelected} onChooseCategory={chooseCategory} />}
       {tab === "revenue" && <>
         <IncomeDistribution data={data} onSelect={setSelected} />
-        <h3>公司披露与估算的历史里程碑</h3>
-        <p className="research-notice">这一组包含 {disclosedCompanies} 家公司的披露或估算信号，多为异时点历史里程碑，ARR 与年化收入口径仍待核对，暂不计算其同期领域均值。币种未确认的记录仅显示原值，不绘制美元长度条。上面的平台MRR统计与这一组分开。</p>
+        <ArrDisclosures data={data} onSelect={setSelected} />
+        <h3>企业披露与估算的历史里程碑</h3>
+        <p className="research-notice">这一组包含 {disclosedCompanies} 家公司的披露或估算信号；企业披露含公司或交易当事方公开材料，披露主体与审计状态见证据。多为异时点历史里程碑，期间收入不直接年化，ARR 与年化收入口径仍待核对，暂不计算其同期领域均值。币种未确认的记录仅显示原值，不绘制美元长度条。上面的平台MRR统计与这一组分开。</p>
         <div className="research-revenue-chart" aria-label="不同时间的公开收入里程碑，线性长度，非同行排名">{observed.map((o) => <div key={o.id}>
           <div><button onClick={() => setSelected(o.company)}>{o.name}</button><strong>{o.bound !== "=" && o.bound}{money(o.value, o.unit)}</strong></div>
           {o.unit === "USD" && <span className="research-bar" data-estimate={o.selection === "estimate" || undefined} aria-hidden="true"><i style={{width: `${o.value / Math.max(1, ...observed.filter((v) => v.unit === "USD").map((v) => v.value)) * 100}%`}} /></span>}
           <small>{o.kind} · {useLabels[o.selection]} · {o.timing}</small>
         </div>)}</div>
-        <div className="research-table-wrap"><table><caption>当前研究数据中的收入可得性，目录项未逐一检索；未建立同期、同龄、同口径比较组</caption><thead><tr><th>业务分类</th><th>公司披露数</th><th>仅估算数</th><th>仅平台数</th><th>暂无可用数字</th><th>同期均值 / 离散度</th></tr></thead><tbody>{summaries.map((c) => <tr key={c.id}><td><button onClick={() => chooseCategory(c.id)}>{c.name}</button></td><td>{c.self}</td><td>{c.estimate}</td><td>{c.platform}</td><td>{c.unknown}</td><td>条件不足</td></tr>)}</tbody></table></div>
+        <div className="research-table-wrap"><table><caption>当前研究数据中的收入可得性，目录项未逐一检索；未建立同期、同龄、同口径比较组</caption><thead><tr><th>业务分类</th><th>企业披露数</th><th>仅估算数</th><th>仅平台数</th><th>暂无可用数字</th><th>同期均值 / 离散度</th></tr></thead><tbody>{summaries.map((c) => <tr key={c.id}><td><button onClick={() => chooseCategory(c.id)}>{c.name}</button></td><td>{c.self}</td><td>{c.estimate}</td><td>{c.platform}</td><td>{c.unknown}</td><td>条件不足</td></tr>)}</tbody></table></div>
         <p className="research-muted">离散度用于筛选值得核查的异常，还需排除公司年龄、口径和披露偏差；不能据此认定财务造假。</p>
       </>}
       {tab === "sources" && <><p className="research-muted">来源链接保留原始位置。相同 URL 的记录可能属于同一披露，不能按记录数计算独立证据数量。</p><ul className="research-evidence-list">{data.evidence.slice(sourcePage * evidencePageSize, (sourcePage + 1) * evidencePageSize).map((e) => <Evidence key={e.id} item={e} />)}</ul>{data.evidence.length > evidencePageSize && <nav className="research-pagination" aria-label="原始证据分页"><button disabled={sourcePage === 0} onClick={() => setSourcePage((n) => n - 1)}>上一页</button><span>{sourcePage + 1} / {Math.ceil(data.evidence.length / evidencePageSize)}</span><button disabled={(sourcePage + 1) * evidencePageSize >= data.evidence.length} onClick={() => setSourcePage((n) => n + 1)}>下一页</button></nav>}</>}

apps/docs/src/components/ArrDisclosures.tsx

@@ -0,0 +1,69 @@
+import { useState } from "react";
+import { evidenceUrl, type ResearchSnapshot } from "@/lib/ai-research";
+import { arrDisclosureCoverage, arrDisclosures, arrFieldGaps, type ArrFieldGap } from "@/lib/research-statistics";
+
+const labels: Record<string, string> = {
+  signal: "企业或当事方披露", estimate: "第三方估算",
+  historical: "历史披露", "historical-estimate": "历史估算",
+};
+const gapLabels: Record<ArrFieldGap, string> = {
+  date: "缺有效财务截至日", currency: "USD 未确认（含其他币种）",
+  amount: "下界、近似或非精确金额", estimate: "第三方估算",
+};
+
+export default function ArrDisclosures({ data, onSelect }: { data: ResearchSnapshot; onSelect: (id: string) => void }) {
+  const [category, setCategory] = useState("");
+  const [year, setYear] = useState("");
+  const [gap, setGap] = useState<ArrFieldGap | "">("");
+  const claims = arrDisclosures(data, { category, foundedYear: year ? Number(year) : undefined });
+  const rows = claims.filter(({ observation }) => !gap || arrFieldGaps(observation, data.asOf).includes(gap));
+  const gapCounts = (Object.keys(gapLabels) as ArrFieldGap[]).map((id) => ({
+    id, count: claims.filter(({ observation }) => arrFieldGaps(observation, data.asOf).includes(id)).length,
+  }));
+  const completeFields = claims.filter(({ observation }) => !arrFieldGaps(observation, data.asOf).length).length;
+  const coverage = arrDisclosureCoverage(data, year ? Number(year) : undefined);
+  const companies = new Set(claims.map((row) => row.company.id));
+  const categories = new Map(data.categories.map((item) => [item.id, item.name]));
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  return <section aria-label="ARR披露分类表">
+    <h3>2025—2026 年成立公司的 ARR 披露</h3>
+    <p className="research-notice">这是跨时点证据表，共 {companies.size} 家公司、{claims.length} 条记录；同一公司的历史里程碑逐条保留，不能相加或视为独立公司。ARR 标签沿用原始披露，未证明经常性收入定义或审计状态；不纳入 MRR 图表，不计算同期行业均值或造假概率。仅收录明确标注的 ARR；定义冲突、目标、承诺、客户和前雇主收入排除，缺失不作零。</p>
+    <div className="research-filters">
+      <label>业务分类<select value={category} onChange={(event) => setCategory(event.target.value)}><option value="">全部分类</option>{data.categories.map((item) => <option key={item.id} value={item.id}>{item.name}</option>)}</select></label>
+      <label>成立年份<select value={year} onChange={(event) => setYear(event.target.value)}><option value="">2025 与 2026</option><option value="2025">2025</option><option value="2026">2026</option></select></label>
+      <label>补证项目<select value={gap} onChange={(event) => setGap(event.target.value as ArrFieldGap | "")}><option value="">全部记录</option>{gapCounts.map((item) => <option key={item.id} value={item.id}>{gapLabels[item.id]}（{item.count} 条）</option>)}</select></label>
+    </div>
+    <div className="research-table-wrap"><table>
+      <caption>ARR 披露覆盖：按公司当前分类及所选成立年份、每家公司计一次。点击分类筛选下方证据表。</caption>
+      <thead><tr><th>业务分类</th><th>已采集公司</th><th>企业或当事方披露</th><th>仅第三方估算</th><th>缺少 ARR</th><th>披露覆盖率</th></tr></thead>
+      <tbody>{coverage.filter((item) => item.companies > 0).map((item) => <tr key={item.id}>
+        <td><button onClick={() => setCategory(item.id)} aria-pressed={category === item.id}>{item.name}</button></td>
+        <td>{item.companies}</td><td>{item.reported}</td><td>{item.estimatedOnly}</td><td>{item.missing}</td>
+        <td>{new Intl.NumberFormat("zh-CN", { style: "percent", maximumFractionDigits: 1 }).format(item.disclosed / item.companies)}</td>
+      </tr>)}</tbody>
+    </table></div>
+    <p className="research-muted">分母含仅有目录资料的公司，覆盖率反映本次采集深度，不代表行业披露率。分类及收费模式采用公司当前资料；公司转向或同时经营多个产品时，历史 ARR 未必属于当前业务，需打开公司核对产品、主体与期间，不能据当前分类重归历史收入。企业披露含历史里程碑，同一公司兼有披露和估算时归企业披露；缺少 ARR 的公司仍可能有其他收入。披露时点、币种与合同定义未对齐，不能将已披露少数公司的均值外推至整个分类，也不能由离散性判断财务造假。</p>
+    <aside className="research-notice" aria-label="ARR比较前的补证检查">
+      <strong>ARR 比较前的补证检查</strong>
+      <p>当前分类及成立年下有 {claims.length} 条披露，{completeFields} 条同时具有有效日期、明确 USD、精确金额且不是第三方估算。补证筛选后显示 {rows.length} 条。</p>
+      <div className="income-options">{gapCounts.map((item) => <button key={item.id} aria-pressed={gap === item.id} onClick={() => setGap(gap === item.id ? "" : item.id)}>{gapLabels[item.id]}：{item.count}</button>)}</div>
+      <p>各项可重叠，不能相加为独立公司数。日期必须是经营指标截至日，网页访问、发布或更新时间不能代替；其他币种保留原值，不擅自换算。基础字段齐全也不证明可比，还需对齐收入定义、业务期间、收费模式、主体与独立验证；这些缺项不作为造假判定。</p>
+    </aside>
+    <div className="research-table-wrap"><table>
+      <caption>金额保留原币种、下界和估算身份；日期栏保留业务截至日缺项</caption>
+      <thead><tr><th>公司 / 当前分类</th><th>当前收费与交付模式</th><th>原始 ARR 声明</th><th>时点</th><th>身份 / 限制</th><th>证据</th></tr></thead>
+      <tbody>{rows.map(({ company, observation: item }) => {
+        const source = evidence.get(item.evidence);
+        const url = source && evidenceUrl(source.url);
+        return <tr key={item.id}>
+          <td><button onClick={() => onSelect(company.id)}>{company.name}</button><small>{categories.get(company.cat)} · {company.year}</small></td>
+          <td>{company.businessModel ?? company.price}</td>
+          <td>{item.bound !== "=" && item.bound}{new Intl.NumberFormat("zh-CN", { maximumFractionDigits: 2 }).format(item.value)} {item.unit}<small>{item.kind}</small></td>
+          <td>{item.timing}</td><td>{labels[item.selection]}<small>{item.reason}</small><small>{arrFieldGaps(item, data.asOf).map((id) => gapLabels[id]).join("；") || "基础字段齐全；定义、期间与主体仍须核实"}</small></td>
+          <td>{url ? <a href={url} target="_blank" rel="noopener noreferrer">{item.evidence} ↗</a> : item.evidence}</td>
+        </tr>;
+      })}</tbody>
+    </table></div>
+    {!rows.length && <p>当前分类、成立年与补证筛选没有符合条件的已记录 ARR 声明；不代表这些公司收入为零。</p>}
+  </section>;
+}

apps/docs/src/components/BusinessModelAssessment.tsx

@@ -0,0 +1,32 @@
+import { businessModelReviewType, evidenceUrl, reviewCompanyIds, type ResearchSnapshot } from "@/lib/ai-research";
+
+export default function BusinessModelAssessment({ data, onSelect, onChooseCategory }: {
+  data: ResearchSnapshot; onSelect: (id: string) => void; onChooseCategory: (id: string) => void;
+}) {
+  const reviews = data.reviews.filter((review) => review.type === businessModelReviewType && review.scope === "companies");
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  return <section aria-labelledby="research-models-title">
+    <h3 id="research-models-title">商业模式初评</h3>
+    <p>从已核个案提炼收费方式、成本约束与下一步验证条件。已知事实与研究判断分列；同一种收费方式可以跨业务分类，以下个案不能代表整个行业。</p>
+    <p className="research-muted">这是基于当前证据的初步评估，不是收入预测或盈利排名。判断能否用于我们的业务，还需用实际客户任务、付费留存与交付成本验证；ARR 或平台 MRR 单独不能证明利润。</p>
+    {reviews.length ? <div className="research-table-wrap"><table>
+      <caption>收费模式与业务分类、公司明细及原始证据相互关联</caption>
+      <thead><tr><th scope="col">模式 / 业务分类</th><th scope="col">已核事实</th><th scope="col">初步判断 / 验证条件</th><th scope="col">参考个案与证据</th></tr></thead>
+      <tbody>{reviews.map((review) => {
+        const companyIds = new Set(reviewCompanyIds(data, review));
+        const companies = data.companies.filter((company) => companyIds.has(company.id));
+        const categories = data.categories.filter((category) => companies.some((company) => company.cat === category.id));
+        return <tr key={review.id}>
+          <td><strong>{review.company}</strong><div>{categories.map((category) => <button key={category.id} onClick={() => onChooseCategory(category.id)}>{category.name}</button>)}</div></td>
+          <td>{review.summary}</td><td>{review.handling}</td>
+          <td><div>{companies.map((company) => <button key={company.id} className="research-company-name" onClick={() => onSelect(company.id)}>{company.name}</button>)}</div>
+            <div>{review.evidenceIds.map((id) => {
+              const item = evidence.get(id); const url = item && evidenceUrl(item.url);
+              return item && url ? <p key={id}><a href={url} target="_blank" rel="noopener noreferrer">{item.title} ↗</a></p> : null;
+            })}</div>
+          </td>
+        </tr>;
+      })}</tbody>
+    </table></div> : <p>当前数据版本尚未记录商业模式初评。</p>}
+  </section>;
+}

apps/docs/src/components/IncomeDistribution.tsx

@@ -1,6 +1,6 @@
 import { useMemo, useState } from "react";
 import type { ResearchSnapshot } from "@/lib/ai-research";
-import { groupIncome, platformIncomeSample } from "@/lib/research-statistics";
+import { compareIncomeGroups, groupIncome, platformIncomeSample } from "@/lib/research-statistics";
 
 const usd = (value: number | null) => value === null ? "—" : new Intl.NumberFormat("zh-CN", {
   style: "currency", currency: "USD", maximumFractionDigits: 0,
@@ -23,6 +23,7 @@ export default function IncomeDistribution({ data, onSelect }: {
   const categoryGroups = [...new Set(groups.map((group) => group.category))].map((category) => ({
     category, rows: groups.filter((group) => group.category === category),
   }));
+  const comparisons = compareIncomeGroups(groups);
   const categories = new Map(data.categories.map((category) => [category.id, category.name]));
   const metric = annualize ? "MRR×12参考年化" : "平台MRR";
   const values = sample.points.map((point) => point.value);
@@ -47,6 +48,20 @@ export default function IncomeDistribution({ data, onSelect }: {
     <p>{splitModels ? "分组：业务类别 × 支付平台 × 收费与交付模式。同一类别下的多行是不同统计组，各自计算均值和中位数。" : "分组：业务类别 × 支付平台。同一平台内的收费模式已合并，不同支付平台仍分别统计。"}</p>
     <p className="research-notice">资料截至 {data.asOf}，纳入 {sample.points.length} 个产品，另有 {sample.excluded.length} 个 MRR 观测未纳入。{annualize ? "乘12只改变显示单位，不证明全年收入、续约或ARR；CV与相对敏感性不变。" : "MRR不等于近30日收款；平台数据连接也不等于财务审计。"}{dateWindow === "all" && " 当前包含不同时间的历史观测，不能称为同期对照。"}</p>
     {sample.points.length ? <>
+      <aside aria-label="当前窗口样本解读" className="research-notice">
+        <strong>当前窗口的样本解读</strong>
+        {comparisons.length ? comparisons.map((comparison) => <div key={comparison.key}>
+          <p>{comparison.provider} · {comparison.model}（仅比较各组 n≥3）：
+            {comparison.leastDispersed.stats.cv === comparison.mostDispersed.stats.cv
+              ? <>各组 CV 相同（{comparison.leastDispersed.stats.cv?.toFixed(3)}），没有离散度高低差异。</>
+              : <>{categories.get(comparison.mostDispersed.category)}更离散（CV {comparison.mostDispersed.stats.cv?.toFixed(3)}），{categories.get(comparison.leastDispersed.category)}较集中（CV {comparison.leastDispersed.stats.cv?.toFixed(3)}）。</>}
+            {comparison.leastSensitive.stats.largestRemovalDrop === comparison.mostSensitive.stats.largestRemovalDrop
+              ? <>各组移除最大值后的均值下降幅度相同（{percent(comparison.leastSensitive.stats.largestRemovalDrop)}）。</>
+              : <>{categories.get(comparison.mostSensitive.category)}的均值更受最大值影响，移除后下降 {percent(comparison.mostSensitive.stats.largestRemovalDrop)}；{categories.get(comparison.leastSensitive.category)}下降 {percent(comparison.leastSensitive.stats.largestRemovalDrop)}。</>}
+          </p>
+        </div>) : <p>当前没有至少两个可对照的统计组。需要相同支付平台、明确的收费模式标签，且各组至少 3 个产品；可调整同步窗口或创立年，不能将单个产品或两点均值当作领域典型。</p>}
+        <p>这些是所选产品样本内的描述，较集中或较不敏感也不证明行业均值可靠。最后同步日并非统一财务结账日，产品年龄、主体独立性、成本和留存仍待核；参考年化也不是已披露 ARR。</p>
+      </aside>
       <figure>
         <div className="research-table-wrap"><svg className="income-distribution-chart" viewBox={`0 0 900 ${groups.length * 70 + 65}`} role="group" aria-label={`${metric}分布，${logScale ? "对数" : "线性"}横轴；圆点为产品，竖线为均值，菱形为中位数`}>
           {ticks.map((tick) => <g key={tick}><line x1={x(tick)} x2={x(tick)} y1={15} y2={groups.length * 70 + 10} stroke="#dfe6e0" /><text x={x(tick)} y={groups.length * 70 + 35} textAnchor="middle" fontSize="11" fill="#637066">{usd(tick)}</text></g>)}
@@ -75,7 +90,7 @@ export default function IncomeDistribution({ data, onSelect }: {
         </svg></div>
         <figcaption>圆点：产品观测　竖线：样本均值　菱形：样本中位数。业务类别标题下各行按支付平台{splitModels && "及收费与交付模式"}分别统计；n为该组产品数。点击点或下表产品名可看原始证据。</figcaption>
       </figure>
-      <div className="research-table-wrap"><table><caption>{metric}样本统计；每行是类别下的一个统计组，CV使用样本标准差</caption><thead><tr><th scope="col">业务类别</th><th scope="col">支付平台 / 收费模式</th><th scope="col">n</th><th scope="col">均值</th><th scope="col">中位数</th><th scope="col">均值/中位数</th><th scope="col">CV</th><th scope="col">去除最大值后的均值 / 下降幅度</th><th scope="col">产品与限制</th></tr></thead>{categoryGroups.map(({ category, rows }) => <tbody key={category}>{rows.map((group, index) => <tr key={group.key}>
+      <div className="research-table-wrap"><table className="income-statistics"><caption>{metric}样本统计；每行是类别下的一个统计组，CV使用样本标准差</caption><thead><tr><th scope="col">业务类别</th><th scope="col">支付平台 / 收费模式</th><th scope="col">n</th><th scope="col">均值</th><th scope="col">中位数</th><th scope="col">均值/中位数</th><th scope="col">CV</th><th scope="col">去除最大值后的均值 / 下降幅度</th><th scope="col">产品与限制</th></tr></thead>{categoryGroups.map(({ category, rows }) => <tbody key={category}>{rows.map((group, index) => <tr key={group.key}>
         {index === 0 && <th scope="rowgroup" rowSpan={rows.length}>{categories.get(category)}<small>{rows.length}个统计组</small></th>}
         <td>{group.provider}<small>{group.model}</small></td><td>{group.stats.n}</td><td>{usd(group.stats.mean)}</td><td>{usd(group.stats.median)}</td><td>{group.stats.meanMedianRatio?.toFixed(2) ?? "—"}</td><td>{group.stats.cv?.toFixed(2) ?? "—"}</td><td>{usd(group.stats.meanWithoutLargest)}<small>{percent(group.stats.largestRemovalDrop)}</small></td>
         <td>{group.points.map((point) => <span key={point.company.id}><button className="research-company-name" onClick={() => onSelect(point.company.id)}>{point.company.name}</button>{" "}</span>)}<small>{group.stats.n < 3 ? "样本少于3，不能据此判断领域典型收入。" : "自选的小样本，须结合产品年龄、成本与留存评估。"}</small></td>

apps/docs/src/components/ResearchCandidates.tsx

@@ -0,0 +1,75 @@
+import { useMemo, useState } from "react";
+import { evidenceUrl, type ResearchCandidateBatch, type ResearchSnapshot } from "@/lib/ai-research";
+
+type CandidateStatus = ResearchCandidateBatch["records"][number]["status"];
+const statusLabels: Record<CandidateStatus, string> = {
+  "pending-year": "成立年或主体待核", "pending-ai": "AI 主营待核", linked: "已关联公司",
+  "existing-outside-cohort": "已关联范围外对照", "outside-cohort": "范围外候选", promotion: "推广活动",
+};
+const pageSize = 20;
+const sourceNames: Record<string, string> = {
+  S01: "Y Combinator", S02: "TechCrunch AI", S04: "Product Hunt", S05: "Sacra",
+  S10: "机器之心", S11: "36氪", S13: "TrustMRR", S14: "a16z Speedrun",
+};
+const sourceLabel = (id: string) => `${id} · ${sourceNames[id] ?? "其他来源"}`;
+
+function SourceLink({ url, label }: { url: string | null; label: string }) {
+  const safe = url && evidenceUrl(url);
+  return safe ? <a href={safe} target="_blank" rel="noopener noreferrer">{label} ↗</a> : <span>{label}（链接未取得）</span>;
+}
+
+export default function ResearchCandidates({ data, onSelect, onChooseCategory }: {
+  data: ResearchSnapshot; onSelect: (id: string) => void; onChooseCategory: (id: string) => void;
+}) {
+  const [source, setSource] = useState("");
+  const [batchId, setBatchId] = useState("");
+  const [category, setCategory] = useState("");
+  const [status, setStatus] = useState<CandidateStatus | "pending" | "">("pending");
+  const [query, setQuery] = useState("");
+  const [page, setPage] = useState(0);
+  const all = useMemo(() => (data.candidateBatches ?? []).flatMap((batch) => batch.records.map((record) => ({ batch, record }))), [data.candidateBatches]);
+  const sources = [...new Set(all.map(({ batch }) => batch.sourceId))].sort();
+  const batches = (data.candidateBatches ?? []).filter((batch) => !source || batch.sourceId === source);
+  const matched = all.filter(({ batch, record }) => (!source || batch.sourceId === source)
+    && (!batchId || batch.id === batchId) && (!category || record.categoryId === category)
+    && `${record.name} ${record.id} ${record.note}`.toLocaleLowerCase().includes(query.trim().toLocaleLowerCase()));
+  const pending = (value: CandidateStatus) => value === "pending-year" || value === "pending-ai";
+  const rows = matched.filter(({ record }) => !status || (status === "pending" ? pending(record.status) : record.status === status));
+  const companies = new Map(data.companies.map((company) => [company.id, company]));
+  const evidence = new Map(data.evidence.map((item) => [item.id, item]));
+  const categories = new Map(data.categories.map((item) => [item.id, item.name]));
+  return <section aria-labelledby="research-candidates-title">
+    <h3 id="research-candidates-title">候选与缺项</h3>
+    <p>展示已登记的有限来源批次及后续核查。默认显示成立年、主体或 AI 主营待核的条目；待核不是确认排除，也不计入公司表。每行是一次发现位置，同一公司或产品可以重复出现。</p>
+    <p className="research-muted">候选初分类与公司表数量分开，来源批次不等全年或全站名单。原始发现范围及后续结论见每行说明；暂无收入资料不作零，推广活动也不算公司。</p>
+    <div className="research-filters">
+      <label className="research-query">搜索候选<input type="search" value={query} placeholder="名称、编号或核查说明" onChange={(event) => { setQuery(event.target.value); setPage(0); }} /></label>
+      <label>发现来源<select value={source} onChange={(event) => { setSource(event.target.value); setBatchId(""); setPage(0); }}><option value="">全部已登记来源</option>{sources.map((id) => <option key={id} value={id}>{sourceLabel(id)}</option>)}</select></label>
+      <label>发现批次<select value={batchId} onChange={(event) => { setBatchId(event.target.value); setPage(0); }}><option value="">全部批次</option>{batches.map((batch) => <option key={batch.id} value={batch.id}>{batch.id}</option>)}</select></label>
+      <label>候选初分类<select value={category} onChange={(event) => { setCategory(event.target.value); setPage(0); }}><option value="">全部分类</option>{data.categories.map((item) => <option key={item.id} value={item.id}>{item.name}</option>)}</select></label>
+      <label>核查状态<select value={status} onChange={(event) => { setStatus(event.target.value as typeof status); setPage(0); }}>
+        <option value="">全部状态（{matched.length}）</option><option value="pending">全部待核（{matched.filter(({ record }) => pending(record.status)).length}）</option>
+        {Object.entries(statusLabels).map(([id, label]) => <option key={id} value={id}>{label}（{matched.filter(({ record }) => record.status === id).length}）</option>)}
+      </select></label>
+    </div>
+    <p role="status">{rows.length} 个发现位置匹配；不是独立公司数。</p>
+    <div className="research-table-wrap"><table>
+      <caption>有限来源候选与处理记录；选择关联公司查看主数据，原始证据直接链接来源</caption>
+      <thead><tr><th scope="col">候选</th><th scope="col">初分类 / 状态</th><th scope="col">来源及范围</th><th scope="col">核查结论 / 缺项</th><th scope="col">关联公司 / 证据</th></tr></thead>
+      <tbody>{rows.slice(page * pageSize, (page + 1) * pageSize).map(({ batch, record }) => {
+        const company = record.companyId && companies.get(record.companyId);
+        return <tr key={record.id}>
+          <td><SourceLink url={record.url} label={record.name} /><small>{record.id}</small></td>
+          <td><button onClick={() => onChooseCategory(record.categoryId)} aria-label={`查看${categories.get(record.categoryId)}分类内的公司`}>{categories.get(record.categoryId)}</button><small>{statusLabels[record.status]}</small></td>
+          <td><SourceLink url={batch.url} label={sourceLabel(batch.sourceId)} /><small>{batch.id} · 读取 {batch.accessedAt}</small><details><summary>发现批次范围</summary><p>{batch.scope}</p><p>{batch.complete ? "该批次标记已完成；不代表来源全站覆盖。" : "该批次仍保留覆盖缺项。"}</p></details></td>
+          <td>{record.note}</td>
+          <td>{company ? <button className="research-company-name" onClick={() => onSelect(company.id)}>{company.name}</button> : <small>尚未关联公司主记录</small>}
+            {record.evidenceIds?.map((id) => { const item = evidence.get(id); return item ? <p key={id}><SourceLink url={item.url} label={id} /></p> : null; })}
+          </td>
+        </tr>;
+      })}</tbody>
+    </table></div>
+    {!rows.length && <p>当前筛选没有已登记的候选记录；不代表该来源或分类没有公司。</p>}
+    {rows.length > pageSize && <nav className="research-pagination" aria-label="候选与缺项分页"><button disabled={page === 0} onClick={() => setPage((value) => value - 1)}>上一页</button><span>{page + 1} / {Math.ceil(rows.length / pageSize)}</span><button disabled={(page + 1) * pageSize >= rows.length} onClick={() => setPage((value) => value + 1)}>下一页</button></nav>}
+  </section>;
+}

apps/docs/src/lib/ai-research.ts

@@ -1,4 +1,5 @@
 export type ResearchCategory = { id: string; name: string; description: string };
+export const businessModelReviewType = "商业模式初评";
 export const technicalReviewLabels = {
   unreviewed: "技术证据尚未专项评审",
   description: "已读技术自述",
@@ -48,7 +49,7 @@ export type ResearchCandidateBatch = {
   records: {
     id: string; name: string; url: string | null; categoryId: string;
     kind: "product" | "company" | "promotion"; profileStatus: "readable" | "unavailable" | "archive-only";
-    status: "pending-year" | "linked" | "existing-outside-cohort" | "outside-cohort" | "promotion";
+    status: "pending-year" | "pending-ai" | "linked" | "existing-outside-cohort" | "outside-cohort" | "promotion";
     companyId?: string; evidenceIds?: string[]; note: string;
   }[];
 };
@@ -125,13 +126,13 @@ export function validateResearch(snapshot: ResearchSnapshot) {
       if (!row.name.trim() || !row.note.trim() || !categories.has(row.categoryId) ||
           !["product", "company", "promotion"].includes(row.kind) ||
           !["readable", "unavailable", "archive-only"].includes(row.profileStatus) ||
-          !["pending-year", "linked", "existing-outside-cohort", "outside-cohort", "promotion"].includes(row.status) ||
+          !["pending-year", "pending-ai", "linked", "existing-outside-cohort", "outside-cohort", "promotion"].includes(row.status) ||
           (row.kind !== "promotion" && (!row.url || !evidenceUrl(row.url))) ||
           (row.url !== null && !evidenceUrl(row.url)) ||
           linked !== !!row.companyId || (row.companyId && !companies.has(row.companyId)) ||
           (row.kind === "promotion") !== (row.status === "promotion") ||
           (row.evidenceIds ?? []).some((id) => !evidence.has(id)) ||
-          (row.status === "outside-cohort" && !row.evidenceIds?.length))
+          (["outside-cohort", "pending-ai"].includes(row.status) && !row.evidenceIds?.length))
         throw new Error(`Invalid research candidate: ${row.id}`);
     }
   }

apps/docs/src/lib/research-statistics.ts

@@ -1,6 +1,50 @@
 import type { ResearchCompany, ResearchObservation, ResearchSnapshot } from "./ai-research";
 
 export type IncomePoint = { company: ResearchCompany; observation: ResearchObservation; value: number };
+
+/** An evidence ledger, never a comparable sample: retain historical claims and estimates separately. */
+export function arrDisclosures(snapshot: ResearchSnapshot, options: { category?: string; foundedYear?: number } = {}) {
+  const companies = new Map(snapshot.companies.map((company) => [company.id, company]));
+  return snapshot.observations.flatMap((observation) => {
+    const company = companies.get(observation.company);
+    if (!company || ![2025, 2026].includes(company.year ?? 0) || observation.metric !== "arr" ||
+        !["signal", "estimate", "historical", "historical-estimate"].includes(observation.selection) ||
+        !Number.isFinite(observation.value) || observation.value < 0 ||
+        (options.category && company.cat !== options.category) ||
+        (options.foundedYear && company.year !== options.foundedYear)) return [];
+    return [{ company, observation }];
+  });
+}
+
+/** Count companies once; missing ARR is a coverage gap, never zero revenue. */
+export function arrDisclosureCoverage(snapshot: ResearchSnapshot, foundedYear?: number) {
+  const rows = arrDisclosures(snapshot, { foundedYear });
+  return snapshot.categories.map((category) => {
+    const companies = snapshot.companies.filter((company) => company.cat === category.id &&
+      [2025, 2026].includes(company.year ?? 0) && (!foundedYear || company.year === foundedYear));
+    const claims = rows.filter((row) => row.company.cat === category.id);
+    const disclosed = new Set(claims.map((row) => row.company.id));
+    const reported = new Set(claims.filter((row) =>
+      ["signal", "historical"].includes(row.observation.selection)).map((row) => row.company.id));
+    return { id: category.id, name: category.name, companies: companies.length,
+      disclosed: disclosed.size, reported: reported.size, estimatedOnly: disclosed.size - reported.size,
+      missing: companies.length - disclosed.size, records: claims.length };
+  });
+}
+
+export type ArrFieldGap = "date" | "currency" | "amount" | "estimate";
+
+/** Prerequisites for inspecting exact, dated USD disclosures; never a comparability or audit verdict. */
+export function arrFieldGaps(observation: ResearchObservation, asOf: string): ArrFieldGap[] {
+  const gaps: ArrFieldGap[] = [];
+  const day = calendarDay(observation.observedAt);
+  const end = calendarDay(asOf);
+  if (day === null || end === null || day > end) gaps.push("date");
+  if (observation.unit !== "USD") gaps.push("currency");
+  if (observation.bound !== "=" || !Number.isFinite(observation.value) || observation.value < 0) gaps.push("amount");
+  if (["estimate", "historical-estimate"].includes(observation.selection)) gaps.push("estimate");
+  return gaps;
+}
 export type IncomeStats = {
   n: number; mean: number; median: number; min: number; max: number;
   sampleDeviation: number | null; cv: number | null; meanMedianRatio: number | null;
@@ -80,3 +124,24 @@ export function groupIncome(points: IncomePoint[], splitModels = true) {
   }
   return [...groups.entries()].map(([key, group]) => ({ key, ...group, stats: describeIncome(group.points.map((point) => point.value))! }));
 }
+
+/** Compare descriptive statistics only within the same provider and delivery model. */
+export function compareIncomeGroups(groups: ReturnType<typeof groupIncome>) {
+  const buckets = new Map<string, typeof groups>();
+  for (const group of groups) {
+    if (group.stats.n < 3 || group.stats.cv === null || group.stats.largestRemovalDrop === null ||
+        group.model === "全部收费模式" || group.model === "收费模式未核验") continue;
+    const key = JSON.stringify([group.provider, group.model]);
+    const bucket = buckets.get(key) ?? [];
+    bucket.push(group);
+    buckets.set(key, bucket);
+  }
+  return [...buckets.entries()].flatMap(([key, rows]) => {
+    if (rows.length < 2) return [];
+    const byCV = [...rows].sort((a, b) => a.stats.cv! - b.stats.cv! || a.key.localeCompare(b.key));
+    const bySensitivity = [...rows].sort((a, b) => a.stats.largestRemovalDrop! - b.stats.largestRemovalDrop! || a.key.localeCompare(b.key));
+    return [{ key, provider: rows[0].provider, model: rows[0].model,
+      leastDispersed: byCV[0], mostDispersed: byCV[byCV.length - 1],
+      leastSensitive: bySensitivity[0], mostSensitive: bySensitivity[bySensitivity.length - 1] }];
+  });
+}

apps/docs/src/styles/globals.css

@@ -1473,6 +1473,13 @@ svg { flex-shrink: 0; }
 
 .research-distribution { margin: 24px 0 38px; }
 .research-distribution figure { margin: 20px 0; }
+.ai-research .income-statistics { min-width: 1200px; }
+/* Row-spanning category headings shift cell indexes between rows. */
+.ai-research .income-statistics td { min-width: 0; }
+.ai-research .income-statistics th[scope="rowgroup"] { min-width: 150px; white-space: nowrap; }
+.ai-research .income-statistics th[scope="rowgroup"] small { display: block; margin-top: 5px; }
+.ai-research .income-statistics td:not(:last-child) { white-space: nowrap; }
+.ai-research .income-statistics td:last-child { min-width: 220px; }
 .income-distribution-chart { display: block; width: 100%; min-width: 900px; height: auto; }
 .income-distribution-chart [role="button"] { cursor: pointer; outline: none; }
 .income-distribution-chart [role="button"]:focus circle:last-child { stroke: #b17725; stroke-width: 3; }

apps/docs/tests/research.test.ts

@@ -8,10 +8,110 @@ import coverageAudit from "../../../content/research/ai-companies.coverage.json"
 import incomeReference from "../../../content/research/ai-income-analysis.json";
 import { readResearchSnapshot } from "../src/lib/ai-research-store";
 import { openSiteDatabase } from "../src/lib/site-database";
-import { categorySummary, evidenceUrl, reviewCompanyIds, validateResearch, type ResearchSnapshot } from "../src/lib/ai-research";
+import { businessModelReviewType, categorySummary, evidenceUrl, reviewCompanyIds, validateResearch, type ResearchSnapshot } from "../src/lib/ai-research";
 import { consumeCode, initializeStore } from "../src/lib/auth-store";
 import { newAuthConfig } from "../src/lib/auth";
-import { describeIncome, groupIncome, platformIncomeSample } from "../src/lib/research-statistics";
+import { arrDisclosureCoverage, arrDisclosures, arrFieldGaps, compareIncomeGroups, describeIncome, groupIncome, platformIncomeSample } from "../src/lib/research-statistics";
+
+test("ARR field checks distinguish missing dates, other currencies, bounds and estimates", () => {
+  const observation = { ...seed.observations[0], metric: "arr", unit: "USD", value: 100,
+    bound: "=", selection: "signal", observedAt: "2026-10-30" } as ResearchSnapshot["observations"][number];
+  assert.deepEqual(arrFieldGaps(observation, "2026-10-31"), []);
+  for (const observedAt of [undefined, "2026-02-30", "2026-11-01"])
+    assert.deepEqual(arrFieldGaps({ ...observation, observedAt }, "2026-10-31"), ["date"]);
+  assert.deepEqual(arrFieldGaps({ ...observation, unit: "EUR" }, "2026-10-31"), ["currency"],
+    "known foreign currencies stay distinct without an invented USD conversion");
+  assert.deepEqual(arrFieldGaps({ ...observation, unit: "原文$，币种待核", bound: ">",
+    selection: "historical-estimate", observedAt: undefined }, "2026-10-31"), ["date", "currency", "amount", "estimate"]);
+  assert.deepEqual(arrFieldGaps({ ...observation, value: Number.NaN }, "2026-10-31"), ["amount"]);
+  assert.deepEqual(arrFieldGaps({ ...observation, value: -1 }, "2026-10-31"), ["amount"]);
+  assert.deepEqual(arrFieldGaps({ ...observation, value: 0 }, "2026-10-31"), [], "disclosed zero is a valid amount");
+  const rows = arrDisclosures(seed as ResearchSnapshot);
+  assert.ok(rows.length > 0);
+  assert.ok(rows.every(({ observation: claim }) => arrFieldGaps(claim, seed.asOf).length > 0),
+    "the current ledger cannot be described as a complete dated exact USD sample");
+});
+
+test("income interpretations compare sufficient samples within a provider and known billing model", () => {
+  const snapshot = structuredClone(seed) as ResearchSnapshot;
+  const makePoints = (cat: string, values: number[], provider = "Stripe", model = "软件订阅") => values.map((value, index) => ({
+    company: { ...snapshot.companies[0], id: `${cat}-${provider}-${model}-${index}`, cat, businessModel: model },
+    observation: { ...snapshot.observations[0], provider }, value,
+  }));
+  const points = [
+    ...makePoints("C04", [100, 100, 100]),
+    ...makePoints("C06", [10, 10, 1000]),
+    ...makePoints("C08", [1, 10000]),
+    ...makePoints("C09", [1, 10, 10000], "RevenueCat"),
+    ...makePoints("C10", [1, 10, 10000], "Stripe", "硬件服务"),
+    ...makePoints("C07", [0, 0, 0]),
+    ...makePoints("C01", [1, 10, 100], "Stripe", "收费模式未核验"),
+  ];
+  const comparisons = compareIncomeGroups(groupIncome(points));
+  assert.equal(comparisons.length, 1);
+  assert.equal(comparisons[0].leastDispersed.category, "C04");
+  assert.equal(comparisons[0].mostDispersed.category, "C06");
+  assert.equal(comparisons[0].leastSensitive.category, "C04");
+  assert.equal(comparisons[0].mostSensitive.category, "C06");
+  assert.deepEqual(compareIncomeGroups(groupIncome(points, false)), [], "merged billing models are not comparable here");
+  const annualized = compareIncomeGroups(groupIncome(points.map((point) => ({ ...point, value: point.value * 12 }))));
+  assert.equal(annualized[0].mostSensitive.category, comparisons[0].mostSensitive.category);
+  assert.ok(Math.abs(annualized[0].mostDispersed.stats.cv! - comparisons[0].mostDispersed.stats.cv!) < 1e-12);
+});
+
+test("ARR coverage counts companies once and separates missing disclosures from zero income", () => {
+  const snapshot = structuredClone(seed) as ResearchSnapshot;
+  snapshot.categories = [{ id: "C07", name: "行业应用", description: "" }];
+  const template = snapshot.companies[0];
+  snapshot.companies = [
+    { ...template, id: "reported", cat: "C07", year: 2025 },
+    { ...template, id: "estimated", cat: "C07", year: 2026 },
+    { ...template, id: "missing", cat: "C07", year: 2025 },
+    { ...template, id: "old", cat: "C07", year: 2024 },
+    { ...template, id: "undated", cat: "C07", year: null },
+  ];
+  const observation = snapshot.observations[0];
+  snapshot.observations = [
+    { ...observation, id: "zero", company: "reported", metric: "arr", selection: "signal", value: 0 },
+    { ...observation, id: "past", company: "reported", metric: "arr", selection: "historical", value: 10 },
+    { ...observation, id: "estimate", company: "reported", metric: "arr", selection: "estimate", value: 20 },
+    { ...observation, id: "only-estimate", company: "estimated", metric: "arr", selection: "historical-estimate", value: 30 },
+    { ...observation, id: "goal", company: "missing", metric: "arr", selection: "excluded", value: 40 },
+    { ...observation, id: "mrr", company: "missing", metric: "mrr", selection: "platform", value: 50 },
+    { ...observation, id: "old-arr", company: "old", metric: "arr", selection: "signal", value: 60 },
+  ];
+  assert.deepEqual(arrDisclosureCoverage(snapshot), [{ id: "C07", name: "行业应用",
+    companies: 3, disclosed: 2, reported: 1, estimatedOnly: 1, missing: 1, records: 4 }]);
+  assert.deepEqual(arrDisclosureCoverage(snapshot, 2025), [{ id: "C07", name: "行业应用",
+    companies: 2, disclosed: 1, reported: 1, estimatedOnly: 0, missing: 1, records: 3 }]);
+});
+
+test("ARR ledger preserves historical estimates without promoting targets, run rates or outside-cohort claims", () => {
+  const snapshot = structuredClone(seed) as ResearchSnapshot;
+  const rows = arrDisclosures(snapshot);
+  const ids = new Set(rows.map((row) => row.observation.id));
+  for (const id of ["O064", "O065", "O066", "O069", "O070", "O071", "O046"])
+    assert.ok(ids.has(id), `${id}: historical claims, estimates and unknown currencies remain visible`);
+  for (const id of ["O050", "O052", "O053", "O055", "SR-O-AI067-runrate", "ROCKET-O-arr", "AQ-O-RUNRATE"])
+    assert.ok(!ids.has(id), `${id}: excluded or ambiguous records cannot enter the ARR ledger`);
+  assert.equal(rows.find((row) => row.observation.id === "O066")?.observation.selection, "historical-estimate");
+  assert.equal(rows.find((row) => row.observation.id === "O046")?.observation.unit, "原文$，币种待核");
+  for (const id of ["O045", "O047"])
+    assert.equal(rows.find((row) => row.observation.id === id)?.observation.unit, "原文$，币种待核",
+      "current subscription prices cannot establish the currency of a historical ARR claim");
+  const category = snapshot.companies.find((company) => company.id === "AI055")!.cat;
+  const filtered = arrDisclosures(snapshot, { category, foundedYear: 2025 });
+  assert.ok(filtered.length > 0);
+  assert.ok(filtered.every((row) => row.company.cat === category && row.company.year === 2025));
+  // A future mistakenly typed target still must not enter the ledger.
+  const target = snapshot.observations.find((item) => item.id === "O052")!;
+  target.metric = "arr";
+  target.company = "AI055";
+  assert.ok(!arrDisclosures(snapshot).some((row) => row.observation.id === target.id));
+  const claim = snapshot.observations.find((item) => item.id === "O065")!;
+  claim.value = Number.NaN;
+  assert.ok(!arrDisclosures(snapshot).some((row) => row.observation.id === claim.id));
+});
 
 test("research revisions share the authentication database and preserve replay limits", () => {
   const dir = mkdtempSync(join(tmpdir(), "site-research-"));
@@ -23,9 +123,14 @@ test("research revisions share the authentication database and preserve replay l
     for (let i = 0; i < 20; i++) consumeCode(config, "invalid");
     assert.equal(consumeCode(config, "invalid"), "limited");
     const initial = readResearchSnapshot();
-    assert.equal(initial.companies.length, 1023);
-    assert.equal(initial.evidence.length, 1318);
-    assert.equal(initial.observations.length, 189);
+    assert.equal(initial.companies.length, 1253);
+    assert.equal(initial.evidence.length, 4894);
+    assert.equal(initial.observations.length, 491);
+    const businessReviews = initial.reviews.filter((review) => review.type === businessModelReviewType);
+    assert.equal(businessReviews.length, 7);
+    assert.deepEqual(businessReviews, seed.reviews.filter((review) => review.type === businessModelReviewType)
+      .sort((a, b) => a.id.localeCompare(b.id)),
+      "business model assessments and company/evidence links persist in the shared database");
     assert.deepEqual(initial.candidateBatches, seed.candidateBatches, "candidate gaps persist in the same database metadata");
     assert.deepEqual(initial.directoryRechecks, seed.directoryRechecks, "follow-up classifications persist without rewriting the discovery audit");
     assert.deepEqual(readResearchSnapshot(), initial, "reopening does not duplicate records");
@@ -64,9 +169,9 @@ test("research validation rejects broken evidence and unsafe source links", () =
 
 test("reviews link explicit companies without turning global or excluded cases into companies", () => {
   const snapshot = structuredClone(seed) as ResearchSnapshot;
-  assert.equal(snapshot.reviews.filter((r) => r.scope === "companies").length, 60);
-  assert.equal(snapshot.reviews.filter((r) => r.scope === "dataset").length, 10);
-  assert.equal(snapshot.reviews.filter((r) => r.scope === "excluded-candidate").length, 31);
+  assert.equal(snapshot.reviews.filter((r) => r.scope === "companies").length, 248);
+  assert.equal(snapshot.reviews.filter((r) => r.scope === "dataset").length, 147);
+  assert.equal(snapshot.reviews.filter((r) => r.scope === "excluded-candidate").length, 54);
   const review = snapshot.reviews.find((r) => r.id === "R4")!;
   assert.deepEqual(reviewCompanyIds(snapshot, review), ["AI007", "AI013", "AI021", "AI034"]);
   assert.deepEqual(reviewCompanyIds(snapshot, snapshot.reviews.find((r) => r.id === "CN-R-PSI")!), []);
@@ -85,20 +190,47 @@ test("reviews link explicit companies without turning global or excluded cases i
 test("disclosure counts exclude client income, biographies, targets and duplicate milestones", () => {
   const snapshot = seed as ResearchSnapshot;
   const summary = categorySummary(snapshot);
-  assert.equal(summary.reduce((n, c) => n + c.count, 0), 1023);
-  assert.equal(summary.reduce((n, c) => n + c.self, 0), 26);
-  assert.equal(summary.reduce((n, c) => n + c.estimate, 0), 1);
-  assert.equal(summary.reduce((n, c) => n + c.platform, 0), 16);
-  assert.equal(summary.reduce((n, c) => n + c.unknown, 0), 980);
+  assert.equal(summary.reduce((n, c) => n + c.count, 0), 1253);
+  assert.equal(summary.reduce((n, c) => n + c.self, 0), 63);
+  assert.equal(summary.reduce((n, c) => n + c.estimate, 0), 2);
+  assert.equal(summary.reduce((n, c) => n + c.platform, 0), 19);
+  assert.equal(summary.reduce((n, c) => n + c.unknown, 0), 1169);
   assert.equal(snapshot.companies.find((c) => c.name === "Rork")?.revenueStatus, "unknown");
   assert.equal(snapshot.companies.find((c) => c.name === "Result")?.revenueStatus, "unknown");
   assert.ok(snapshot.observations.filter((o) => o.selection === "signal").every((o) => o.scope === "公司整体"));
+  const arena = snapshot.observations.find((o) => o.id === "COARENA-O-CUMULATIVE")!;
+  assert.equal(arena.selection, "historical", "cumulative launch revenue is not a recurring company-wide signal");
+  assert.equal(arena.comparisonGroup, null);
+  assert.ok(arena.metric !== "mrr" && arena.metric !== "arr", "a two-week cumulative figure cannot become MRR or ARR");
+});
+
+test("service run rates, legacy freight revenue and paid adoption do not become platform ARR", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const runRate = snapshot.observations.find((o) => o.id === "INDUSTRY11-O-LOCKE-RUNRATE")!;
+  assert.equal(runRate.metric, "annualized-revenue");
+  assert.equal(runRate.selection, "historical");
+  assert.equal(runRate.observedAt, undefined);
+  assert.equal(runRate.comparisonGroup, null);
+  assert.equal(snapshot.companies.find((c) => c.id === "YC33283")?.revenueStatus, "self");
+  for (const id of ["TRUEMILE-OLD-REV", "VELTHA-DEMO", "LANTERN-OLD-REV"]) {
+    const observation = snapshot.observations.find((o) => o.id === `INDUSTRY11-O-${id}`)!;
+    assert.equal(observation.selection, "excluded");
+    assert.equal(observation.metric, undefined);
+    assert.equal(observation.comparisonGroup, null);
+  }
+  for (const [id, company] of [["VERDANT-DEPLOY", "YC32189"], ["SUTRIX-PAID", "YC36576"]]) {
+    const observation = snapshot.observations.find((o) => o.id === `INDUSTRY11-O-${id}`)!;
+    assert.equal(observation.selection, "adoption");
+    assert.equal(observation.metric, undefined);
+    assert.equal(snapshot.companies.find((c) => c.id === company)?.revenueStatus, "unknown");
+  }
+  assert.ok(platformIncomeSample(snapshot, { windowDays: 90 }).points.every((point) => !point.observation.id.startsWith("INDUSTRY11-")));
 });
 
 test("technical reviews keep accessible materials separate from tested capabilities", () => {
   const snapshot = structuredClone(seed) as ResearchSnapshot;
-  assert.equal(snapshot.companies.filter((c) => c.technicalReview).length, 16);
-  assert.equal(snapshot.companies.filter((c) => c.technicalReview?.level === "public-artifact").length, 6);
+  assert.equal(snapshot.companies.filter((c) => c.technicalReview).length, 244);
+  assert.equal(snapshot.companies.filter((c) => c.technicalReview?.level === "public-artifact").length, 28);
   assert.equal(snapshot.companies.filter((c) => c.technicalReview?.level === "independent-test").length, 1);
   assert.equal(snapshot.companies.find((c) => c.id === "AI051")?.revenueStatus, "unknown");
   assert.match(snapshot.companies.find((c) => c.id === "AI051")!.technicalReview!.testScope!, /Sep 2026/);
@@ -137,6 +269,49 @@ test("ambiguous ARR claims retain currency, precision and attribution limits", (
   assert.equal(snapshot.companies.find((c) => c.id === "AI070")?.revenueStatus, "unknown");
   assert.equal(snapshot.observations.filter((o) => o.company === "AI070").length, 0,
     "unreadable announcement summaries do not become numeric ARR");
+  const waitlist = snapshot.observations.find((o) => o.id === "SR25-O-OMI-WAITLIST")!;
+  assert.equal(waitlist.selection, "excluded", "waitlist conversions are not paid recurring revenue");
+  assert.equal(snapshot.companies.find((c) => c.id === waitlist.company)?.revenueStatus, "unknown");
+  const bluma = snapshot.observations.find((o) => o.id === "BLUMA-O-HISTORICAL-MRR")!;
+  assert.equal(bluma.selection, "historical");
+  assert.ok(bluma.unit !== "USD" && !bluma.observedAt && bluma.comparisonGroup === null);
+  const native = snapshot.observations.find((o) => o.id === "NATIVE-O-ARR-CLAIM")!;
+  assert.equal(native.selection, "crosscheck");
+  assert.ok(!native.metric && !native.observedAt && native.comparisonGroup === null);
+  const marketVolume = snapshot.observations.find((o) => o.id === "PALISADE-O-GMV")!;
+  assert.equal(marketVolume.selection, "excluded", "customer GMV must not establish company revenue or ARR");
+  assert.equal(snapshot.companies.find((c) => c.id === marketVolume.company)?.revenueStatus, "unknown");
+  const pingo = snapshot.observations.find((o) => o.id === "PINGO-O-MRR-JUL25")!;
+  assert.equal(pingo.selection, "historical", "a July 2025 self-report is not a current payment-platform sample");
+  assert.ok(pingo.unit !== "USD" && !pingo.observedAt && pingo.comparisonGroup === null);
+  const designService = snapshot.observations.find((o) => o.id === "SEMIOTIC-O-LAUNCH-REVENUE")!;
+  assert.equal(designService.selection, "historical");
+  assert.ok(!designService.metric && designService.comparisonGroup === null, "four-week service revenue is not MRR or ARR");
+  const restaurantAdoption = snapshot.observations.filter((o) => o.company === "YC32815");
+  assert.ok(restaurantAdoption.length > 0 && restaurantAdoption.every((o) => o.selection === "adoption" && !o.metric),
+    "restaurant count and list price must not produce an invented MRR observation");
+  const fishPilot = snapshot.observations.find((o) => o.id === "OCTAPULSE-O-CONTRACT")!;
+  assert.equal(fishPilot.selection, "excluded", "a paid pilot or annual contract does not establish recognized recurring revenue");
+  assert.equal(snapshot.companies.find((c) => c.id === fishPilot.company)?.revenueStatus, "unknown");
+  assert.equal(fishPilot.comparisonGroup, null);
+  assert.notEqual(fishPilot.unit, "USD", "six-figure wording does not establish the currency");
+  assert.ok(!fishPilot.metric && !fishPilot.observedAt, "the publication date is not the financial reporting date");
+  const cedar = snapshot.observations.filter((o) => o.company === "AI102");
+  assert.ok(cedar.every((o) => o.selection !== "signal" && o.comparisonGroup === null),
+    "closed and contracted amounts without recognition or period do not establish ARR");
+  assert.equal(snapshot.observations.find((o) => o.id === "SR25-O-LOOPS-CUSTOMER-SALES")?.selection, "excluded");
+  const portfolio = snapshot.observations.filter((o) => o.company === "AI100");
+  assert.deepEqual(portfolio.filter((o) => o.selection === "signal").map((o) => o.id), ["SR25-O-DRS-PORTFOLIO"],
+    "a product milestone must not be added to its company portfolio as another company");
+  const speedrun2025 = snapshot.observations.filter((o) => o.id.startsWith("SR25-O-"));
+  assert.ok(speedrun2025.every((o) =>
+    o.unit !== "USD" && !o.observedAt && o.comparisonGroup === null),
+  "access dates and dollar symbols must not invent dated USD income samples");
+  assert.deepEqual(speedrun2025.filter((o) => o.metric).map((o) => [o.id, o.metric]).sort(),
+    [["SR25-O-DRS-PORTFOLIO", "arr"], ["SR25-O-LOOPS-ARR", "arr"]],
+    "explicit company ARR claims retain their metric without inferring currency or reporting dates");
+  assert.ok(platformIncomeSample(snapshot, { windowDays: null }).points.every((point) =>
+    !point.observation.id.startsWith("SR25-O-")), "undated ARR claims never enter platform MRR comparisons");
 });
 
 // Retractions and forecasts must stay visible for review without becoming revenue signals.
@@ -179,8 +354,22 @@ test("directory coverage reconciles every candidate and keeps profile-only incom
     assert.ok(company.profileOnly || company.directoryOrigin === "yc-2025-2026");
     assert.equal(company.url, row.source);
     if (company.profileOnly) assert.equal(company.revenueStatus, "unknown");
-    assert.equal(company.year, row.foundedYear);
-    assert.ok([2025, 2026].includes(company.year!));
+    if (company.id === "YC31370") {
+      assert.equal(row.foundedYear, 2025, "original discovery remains unchanged");
+      assert.equal(company.year, null, "a pivot year cannot settle the company founding year");
+      assert.ok(snapshot.reviews.some((review) => review.id === "DAYJOB-R-COHORT" && review.companyIds?.includes(company.id)));
+    } else if (company.id === "YC36279") {
+      assert.equal(row.foundedYear, 2026, "original Native discovery remains unchanged");
+      assert.equal(company.year, null, "a model launch and YC date cannot settle the older business founding year");
+      assert.ok(snapshot.reviews.some((review) => review.id === "NATIVE-R-COHORT" && review.companyIds?.includes(company.id)));
+    } else if (company.id === "YC31470") {
+      assert.equal(row.foundedYear, 2025, "original Tasklet discovery remains unchanged");
+      assert.equal(company.year, null, "a new product cannot establish a new company founding year");
+      assert.ok(snapshot.reviews.some((review) => review.id === "ENTERPRISE3-R-TASKLET" && review.companyIds?.includes(company.id)));
+    } else {
+      assert.equal(company.year, row.foundedYear);
+      assert.ok([2025, 2026].includes(company.year!));
+    }
   }
   const invalid = structuredClone(snapshot);
   invalid.coverage!.newProfiles += 1;
@@ -194,6 +383,20 @@ test("directory coverage reconciles every candidate and keeps profile-only incom
   assert.throws(() => validateResearch(lostOrigin), /coverage accounting/);
 });
 
+test("business pivots and procurement volume do not become comparable startup ARR", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const dayjob = snapshot.companies.find((company) => company.id === "YC31370")!;
+  assert.equal(dayjob.year, null);
+  assert.equal(dayjob.directoryOrigin, "yc-2025-2026");
+  const milestones = snapshot.observations.filter((observation) => observation.company === dayjob.id);
+  assert.deepEqual(milestones.filter((observation) => observation.selection === "crosscheck").map((observation) => observation.value).sort(), [496000, 653000]);
+  assert.ok(milestones.every((observation) => observation.comparisonGroup === null && !observation.metric && !observation.observedAt));
+  assert.ok(milestones.filter((observation) => [150000, 800000].includes(observation.value)).every((observation) => observation.selection === "excluded"));
+  const procurement = snapshot.observations.find((observation) => observation.id === "SAUDARA-O-SOURCING")!;
+  assert.equal(procurement.selection, "excluded");
+  assert.equal(procurement.comparisonGroup, null);
+});
+
 test("income descriptions report sample dispersion and maximum-value sensitivity", () => {
   const stats = describeIncome([100, 200, 300])!;
   assert.equal(stats.mean, 200);
@@ -215,14 +418,21 @@ test("income descriptions report sample dispersion and maximum-value sensitivity
 test("platform samples exclude stale data and keep ARR, estimates and collections separate", () => {
   const snapshot = seed as ResearchSnapshot;
   const recent = platformIncomeSample(snapshot, { windowDays: 7 });
-  assert.equal(recent.points.length, 14);
-  assert.equal(recent.excluded.length, 3);
+  assert.equal(recent.points.length, 16);
+  assert.equal(recent.excluded.length, 4);
   assert.ok(recent.points.every((p) => p.observation.metric === "mrr" && p.observation.selection === "platform"));
-  assert.deepEqual(new Set(recent.excluded.map((p) => p.observation.company)), new Set(["AI016", "MR013", "MR014"]));
+  assert.deepEqual(new Set(recent.excluded.map((p) => p.observation.company)), new Set(["AI016", "MR013", "MR014", "MR017"]));
   assert.ok(recent.points.some((p) => p.company.name === "TrackAI" && p.observation.connectionExpired), "dated recent value is not a claim of active connection");
   const older = platformIncomeSample(snapshot, { windowDays: 90 });
-  assert.equal(older.points.length, 15);
+  assert.equal(older.points.length, 18);
   assert.ok(older.points.some((p) => p.company.id === "AI016"));
+  assert.ok(!recent.points.some((p) => p.company.id === "MR017"));
+  assert.ok(older.points.some((p) => p.company.id === "MR017" && p.observation.connectionExpired),
+    "an expired connection retains its historical observation date in the wider window");
+  assert.equal(recent.points.find((p) => p.company.id === "MR018")?.value, 67864,
+    "an older Markdown cache must not replace or duplicate the later web snapshot");
+  assert.equal(recent.points.find((p) => p.company.id === "MR019")?.value, 65431,
+    "period-table revenue and revenue snapshots must not replace MRR");
   const scaled = platformIncomeSample(snapshot, { windowDays: 7, annualize: true });
   const monthlyGroups = groupIncome(recent.points);
   const annualGroups = groupIncome(scaled.points);
@@ -234,7 +444,7 @@ test("platform samples exclude stale data and keep ARR, estimates and collection
   const invalid = structuredClone(snapshot);
   invalid.observations = invalid.observations.filter((o) => o.company === "MR001" && o.metric === "mrr");
   const first = invalid.observations[0];
-  invalid.observations.push({ ...first, id: "future", observedAt: "2026-11-01", value: 999999 });
+  invalid.observations.push({ ...first, id: "future", observedAt: "2026-11-02", value: 999999 });
   invalid.observations.push({ ...first, id: "bad-date", observedAt: "2026-02-30", value: 999999 });
   invalid.observations.push({ ...first, id: "currency", unit: "EUR", value: 999999 });
   invalid.observations.push({ ...first, id: "older", observedAt: "2026-10-28", value: 1 });
@@ -280,6 +490,146 @@ test("product launch candidates preserve unresolved years and many launches per
   assert.throws(() => validateResearch(snapshot), /research candidate/);
 });
 
+test("the public SR007 roster preserves unique entries and Alven's older operator conflict", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((item) => item.id === "SR007-public-roster-2026-1")!;
+  assert.equal(batch.complete, false, "one public default cohort is not all source coverage");
+  assert.equal(batch.records.length, 79);
+  assert.equal(new Set(batch.records.map((row) => row.url)).size, 79);
+  const alven = batch.records.find((row) => row.name === "Alven")!;
+  assert.equal(alven.status, "pending-year");
+  assert.equal(alven.companyId, undefined);
+  assert.ok(alven.evidenceIds?.includes("SR007R-ALVEN-REG"));
+  assert.ok(!snapshot.observations.some((item) => item.evidence.startsWith("SR007R-")),
+    "roster claims, deployment plans and per-unit prices are not new financial observations");
+  const coherence = batch.records.find((row) => row.name === "Coherence Health")!;
+  assert.equal(coherence.status, "pending-ai", "a new coaching business still needs current AI delivery evidence");
+  assert.equal(coherence.companyId, undefined);
+  const followup = snapshot.observations.filter((item) => item.id.startsWith("SR007F2-"));
+  assert.equal(followup.length, 4);
+  assert.ok(followup.every((item) => ["excluded", "adoption"].includes(item.selection) && !item.metric && !item.observedAt));
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id.startsWith("SR007F2-")),
+    "contracted ARR and pipeline are not live ARR disclosures");
+  const thirdFollowup = snapshot.observations.filter((item) => item.id.startsWith("SR007F3-"));
+  assert.equal(thirdFollowup.length, 6);
+  assert.equal(thirdFollowup.filter((item) => item.selection === "excluded").length, 3);
+  assert.equal(thirdFollowup.filter((item) => item.selection === "adoption").length, 3);
+  assert.ok(thirdFollowup.every((item) => !item.metric && !item.observedAt));
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id.startsWith("SR007F3-")),
+    "projected ARR, connected customer assets and prospective contracts are not realized ARR");
+  assert.equal(snapshot.companies.find((item) => item.id === "AI121")!.cat, "C02",
+    "training data and environments are infrastructure rather than demonstrated foundation models");
+  const embrasure = snapshot.companies.find((item) => item.id === "AI122")!;
+  assert.equal(embrasure.technicalReview!.level, "public-artifact");
+  assert.ok(embrasure.technicalReview!.evidenceIds.includes("SR007F3-EMBRASURE-FLOW"),
+    "the public connector supports artifact availability, not independently reproduced warehouse performance");
+  for (const name of ["KIRA", "Leonidas", "Mecone", "PhotonSpear"]) {
+    const candidate = batch.records.find((row) => row.name === name)!;
+    assert.equal(candidate.status, "pending-ai",
+      "serving AI customers or using software controls is not evidence of an AI business");
+    assert.equal(candidate.companyId, undefined);
+  }
+  const maestroClaims = arrDisclosures(snapshot).filter(({ company }) => company.id === "AI130");
+  assert.equal(maestroClaims.length, 2, "keep the two original ARR milestones in the evidence ledger");
+  assert.ok(maestroClaims.every(({ observation }) => observation.selection === "historical" &&
+    arrFieldGaps(observation, snapshot.asOf).includes("date") &&
+    arrFieldGaps(observation, snapshot.asOf).includes("currency")));
+  const cumulative = snapshot.observations.find((item) => item.id === "SR007F4-O-MAESTRO-CUMULATIVE")!;
+  assert.equal(cumulative.selection, "historical");
+  assert.equal(cumulative.metric, undefined, "nine-month cumulative revenue cannot become ARR");
+  const contracted = snapshot.observations.find((item) => item.id === "SR007F4-O-HOID-CONTRACT")!;
+  assert.equal(contracted.selection, "excluded");
+  assert.equal(contracted.metric, undefined, "contracted run rate is not realized ARR");
+  const openwhispr = snapshot.companies.find((item) => item.id === "AI135")!;
+  assert.equal(openwhispr.technicalReview!.level, "public-artifact");
+  assert.ok(openwhispr.technicalReview!.evidenceIds.includes("SR007F5-OPENWHISPR-REPO"));
+  const voiceArr = arrDisclosures(snapshot).find(({ company }) => company.id === openwhispr.id)!.observation;
+  assert.equal(voiceArr.value, 120000);
+  assert.equal(voiceArr.bound, "≥");
+  assert.ok(arrFieldGaps(voiceArr, snapshot.asOf).includes("date"));
+  assert.ok(arrFieldGaps(voiceArr, snapshot.asOf).includes("currency"),
+    "current plan prices cannot supply the currency of an undated investor ARR claim");
+  const voiceUsers = snapshot.observations.find((item) => item.id === "SR007F5-O-OPENWHISPR-DAU")!;
+  assert.equal(voiceUsers.selection, "adoption", "daily active users are not paying subscribers");
+  assert.equal(voiceUsers.metric, undefined);
+  const salesLive = snapshot.observations.find((item) => item.id === "SR007F6-O-OTHELLO-LIVE")!;
+  assert.equal(salesLive.metric, "arr");
+  assert.equal(salesLive.value, 600000, "live ARR must not include contracted ARR or pipeline");
+  for (const suffix of ["CONTRACT", "PIPELINE"]) {
+    const prospective = snapshot.observations.find((item) => item.id === `SR007F6-O-OTHELLO-${suffix}`)!;
+    assert.equal(prospective.selection, "excluded");
+    assert.equal(prospective.metric, undefined);
+  }
+  const professionalService = snapshot.observations.find((item) => item.id === "SR007F6-O-PARCA-RUNRATE")!;
+  assert.equal(professionalService.metric, "annualized-revenue");
+  assert.equal(professionalService.selection, "historical");
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id === professionalService.id),
+    "annualized professional-service revenue is not recurring subscription ARR");
+  assert.equal(snapshot.companies.find((item) => item.id === "AI137")!.technicalReview!.level, "description",
+    "a preliminary external ranking with unresolved methodology is not complete independent verification");
+  const contractFollowup = snapshot.observations.filter((item) => item.id.startsWith("SR007F7-"));
+  assert.equal(contractFollowup.filter((item) => item.selection === "excluded").length, 4);
+  assert.equal(contractFollowup.filter((item) => item.selection === "adoption").length, 1);
+  assert.ok(contractFollowup.every((item) => !item.metric && !item.observedAt));
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id.startsWith("SR007F7-")),
+    "contracted revenue and potential customers' active users cannot become realized ARR");
+  assert.equal(snapshot.companies.find((item) => item.id === "AI144")!.revenueStatus, "unknown",
+    "free current SDK terms and a contracted ARR claim need reconciliation before labeling realized revenue");
+  const scaledrive = batch.records.find((row) => row.name === "scaledrive.ai")!;
+  assert.equal(scaledrive.status, "outside-cohort", "SR007 membership cannot replace a stated 2024 founding year");
+  assert.equal(scaledrive.companyId, undefined);
+  assert.ok(scaledrive.evidenceIds?.includes("SR007F8-SCALEDRIVE-SR"));
+  for (const id of ["AI147", "AI150"]) {
+    assert.equal(snapshot.companies.find((item) => item.id === id)!.technicalReview!.level, "description",
+      "self-hosting and SDK documentation do not establish correctly attributed public source code");
+    assert.equal(snapshot.companies.find((item) => item.id === id)!.revenueStatus, "unknown");
+  }
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id.startsWith("SR007F8-")),
+    "previous-company ARR and financial amounts in document demonstrations are not current revenue");
+  assert.equal(batch.records.filter((row) => row.status === "linked").length, 71);
+  assert.equal(batch.records.filter((row) => row.profileStatus === "archive-only").length, 0,
+    "all reused independent profiles have now been explicitly reread");
+  assert.equal(batch.records.filter((row) => row.profileStatus === "readable").length, 79);
+  const ninthFollowup = snapshot.observations.filter((item) => item.id.startsWith("SR007F9-"));
+  assert.equal(ninthFollowup.length, 10);
+  assert.equal(ninthFollowup.filter((item) => item.selection === "excluded").length, 6);
+  assert.equal(ninthFollowup.filter((item) => item.selection === "adoption").length, 3);
+  const industrialRunRate = ninthFollowup.find((item) => item.id === "SR007F9-O-VALENT-ANNUALIZED")!;
+  assert.equal(industrialRunRate.metric, "annualized-revenue");
+  assert.equal(industrialRunRate.selection, "historical");
+  assert.ok(ninthFollowup.every((item) => !item.observedAt),
+    "access dates and weeks since launch cannot supply precise financial dates");
+  assert.ok(ninthFollowup.filter((item) => item !== industrialRunRate).every((item) => !item.metric));
+  assert.ok(!arrDisclosures(snapshot).some(({ observation }) => observation.id.startsWith("SR007F9-")),
+    "annual contracts without fulfillment evidence, funding, annualized revenue and pipeline remain outside live ARR");
+  for (const id of ["AI152", "AI158"]) {
+    assert.equal(snapshot.companies.find((item) => item.id === id)!.revenueStatus, "unknown",
+      "contracted labels alone cannot establish realized revenue");
+  }
+  assert.equal(snapshot.companies.find((item) => item.id === "AI161")!.cat, "C02",
+    "enterprise workload model specialization is classified by delivery rather than frontier-model slogans");
+  const rereadProfiles = snapshot.evidence.filter((item) => item.id.startsWith("SR007F10-P-"));
+  assert.equal(rereadProfiles.length, 17);
+  assert.equal(new Set(rereadProfiles.map((item) => item.companyId)).size, 17);
+  for (const evidence of rereadProfiles) {
+    const candidate = batch.records.find((row) => row.companyId === evidence.companyId)!;
+    assert.equal(candidate.url, evidence.url);
+    assert.ok(candidate.evidenceIds?.includes(evidence.id), "each reread has an exact profile link");
+  }
+  assert.ok(!snapshot.observations.some((item) => item.id.startsWith("SR007F10-")),
+    "rereading existing financial milestones must not introduce duplicate observations");
+  for (const id of ["AI067", "AI069"]) {
+    assert.equal(snapshot.companies.find((item) => item.id === id)!.cat, "C02",
+      "current hosted-model and training-data delivery are infrastructure");
+  }
+  const oldWorkflowArr = snapshot.observations.find((item) => item.id === "SR-O-AI069-arr")!;
+  assert.equal(oldWorkflowArr.evidence, "SR-E-infragrid", "retain the original workflow revenue evidence");
+  assert.equal(oldWorkflowArr.value, 302000);
+  assert.equal(oldWorkflowArr.observedAt, undefined);
+  assert.ok(snapshot.companies.find((item) => item.id === "AI069")!.notes.includes("历史ARR归属不清"),
+    "a current category change must retain the unresolved historical product attribution");
+});
+
 test("annual launch archives exclude older companies only with evidence and reuse existing records", () => {
   const snapshot = structuredClone(seed) as ResearchSnapshot;
   const batch = snapshot.candidateBatches!.find((b) => b.id === "PH-2025-visible-1")!;
@@ -307,7 +657,7 @@ test("unreconciled directory financials remain source reviews instead of income
   assert.ok(review.evidenceIds.includes("PH-TANA-DR"));
   assert.equal(reviewCompanyIds(snapshot, review).length, 0);
   assert.ok(!snapshot.observations.some((o) => o.evidence === "PH-TANA-DR"));
-  assert.equal(snapshot.candidateBatches!.flatMap((b) => b.records).filter((r) => r.status === "pending-year").length, 11);
+  assert.equal(snapshot.candidateBatches!.flatMap((b) => b.records).filter((r) => r.status === "pending-year").length, 131);
 });
 
 test("product pivots and founder histories do not become new company years or current revenue", () => {
@@ -320,6 +670,15 @@ test("product pivots and founder histories do not become new company years or cu
   assert.equal(candidate.companyId, pivot.id);
   assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((point) => point.company.id === pivot.id));
   assert.ok(!snapshot.observations.some((o) => ["PH-FUNDRAISLY-ABOUT", "LF-PIVOT", "LF-HISTORY"].includes(o.evidence)), "other ventures' ARR, users and funding must not transfer");
+  const candidates = snapshot.candidateBatches!.flatMap((b) => b.records);
+  assert.equal(candidates.find((r) => r.id === "PH26-12")?.status, "outside-cohort",
+    "a brand iteration with an earlier business chain is not a new company");
+  const happycapy = candidates.find((r) => r.id === "PH26-04")!;
+  assert.ok(happycapy.evidenceIds?.includes("FOLLOWUP-CAPY-ACRA"));
+  assert.equal(happycapy.status, "pending-year", "a seller's registration does not establish a new global business");
+  assert.equal(happycapy.companyId, undefined);
+  assert.ok(!snapshot.observations.some((o) => o.evidence.startsWith("FOLLOWUP-")),
+    "registration, funding, app-store prices and client amounts are not company income");
 });
 
 test("follow-up evidence can admit an uncertain directory candidate without rewriting its original status", () => {
@@ -335,6 +694,337 @@ test("follow-up evidence can admit an uncertain directory candidate without rewr
   assert.throws(() => validateResearch(snapshot), /directory recheck/);
 });
 
+test("AI relevance follow-ups retain unresolved businesses and reuse existing company identities", () => {
+  const snapshot = structuredClone(seed) as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-1")!;
+  assert.equal(batch.complete, false);
+  assert.equal(batch.records.length, 24);
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(0, 24);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  const linked = batch.records.filter((r) => r.status === "linked");
+  assert.equal(linked.length, 17);
+  assert.equal(linked.filter((r) => r.companyId!.startsWith("YC")).length, 14);
+  for (const [name, id] of [["Bud", "AI070"], ["Prism", "AI030"], ["Parametric", "AI014"]]) {
+    assert.equal(linked.find((r) => r.name === name)?.companyId, id);
+  }
+  assert.equal(snapshot.companies.filter((c) => c.profileOnly || c.directoryOrigin === "yc-2025-2026").length, 928);
+  for (const row of linked) {
+    const ycId = Number(row.id.replace("YC-AI1-", ""));
+    assert.equal(snapshot.directoryRechecks!.find((r) => r.ycId === ycId)?.companyId, row.companyId);
+  }
+  const pending = batch.records.filter((r) => r.status === "pending-ai");
+  assert.equal(pending.length, 7);
+  assert.ok(pending.every((r) => !r.companyId && r.evidenceIds!.length && /2025/.test(r.note)));
+  assert.match(pending.find((r) => r.name === "Better Auth")!.note, /AI.*模块/);
+  validateResearch(snapshot);
+  pending[0].companyId = "AI014";
+  assert.throws(() => validateResearch(snapshot), /research candidate/);
+  delete pending[0].companyId;
+  pending[0].evidenceIds = [];
+  assert.throws(() => validateResearch(snapshot), /research candidate/);
+});
+
+test("historical monthly revenue and customer examples do not enter recurring platform comparisons", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const monthly = snapshot.observations.find((o) => o.id === "UNCLEAR1-O-SCALAR-MONTHLY")!;
+  assert.equal(monthly.value, 74000);
+  assert.equal(monthly.selection, "historical");
+  assert.equal(monthly.metric, undefined, "monthly revenue alone does not establish MRR or ARR");
+  assert.equal(monthly.comparisonGroup, null);
+  assert.equal(snapshot.companies.find((c) => c.id === "YC30514")!.revenueStatus, "self");
+  for (const id of ["AMBY-CLIENT", "NIXO-EXAMPLE", "AUTOSTEP-WASTE"]) {
+    assert.equal(snapshot.observations.find((o) => o.id === `UNCLEAR1-O-${id}`)!.selection, "excluded");
+  }
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR1-")));
+  const cua = snapshot.companies.find((c) => c.id === "YC30420")!;
+  assert.equal(cua.cat, "C03");
+  assert.equal(cua.revenueStatus, "unknown");
+  assert.match(cua.businessModel!, /自备计算机/);
+  assert.equal(cua.technicalReview!.level, "public-artifact");
+});
+
+test("a second uncertain cohort admits evidenced AI tasks while preserving autonomy and finance gaps", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-2")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(24, 48);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 14);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 10);
+  for (const name of ["Albacore Inc.", "Perseus Defense", "Munify", "Ruvo"]) {
+    const row = batch.records.find((r) => r.name === name)!;
+    assert.equal(row.status, "pending-ai");
+    assert.equal(row.companyId, undefined, "autonomy, a domain or finance automation alone is insufficient");
+    assert.ok(row.evidenceIds!.length);
+  }
+  const signals = snapshot.companies.find((c) => c.id === "YC30639")!;
+  assert.equal(signals.year, 2026, "retain the company year instead of assigning the batch's majority year");
+  assert.equal(signals.cat, "C06");
+  assert.equal(snapshot.companies.find((c) => c.id === "YC30846")!.cat, "C03");
+  assert.match(snapshot.companies.find((c) => c.id === "YC30821")!.business, /远端推理/);
+  assert.equal(snapshot.companies.find((c) => c.id === "YC30933")!.technicalReview!.level, "description",
+    "vendor clinical agreement is not an independent validation");
+  const amounts = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR2-"));
+  assert.equal(amounts.length, 8);
+  assert.ok(amounts.every((o) => ["excluded", "adoption"].includes(o.selection) && !o.metric && o.comparisonGroup === null));
+  assert.equal(amounts.find((o) => o.id.endsWith("WARDSTONE-GRANT"))!.selection, "excluded");
+  assert.equal(amounts.find((o) => o.id.endsWith("SOLVA-OLD-PREMIUM"))!.selection, "excluded");
+  assert.ok(batch.records.filter((r) => r.companyId).every((r) => snapshot.companies.find((c) => c.id === r.companyId)!.revenueStatus === "unknown"));
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR2-")));
+});
+
+test("the third uncertain cohort retains shutdowns, unknown scale and mixed data-service economics", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-3")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(48, 72);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 15);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 9);
+  for (const name of ["Mirabelle", "Kyten Technologies"]) {
+    assert.equal(batch.records.find((r) => r.name === name)!.companyId, undefined);
+  }
+  const klaus = snapshot.companies.find((c) => c.id === "YC31084")!;
+  assert.equal(klaus.revenueStatus, "unknown", "a shutdown must not manufacture a zero revenue report");
+  assert.match(klaus.stage, /2026-09-01停服/);
+  assert.ok(!snapshot.observations.some((o) => o.company === klaus.id));
+  const openspec = snapshot.companies.find((c) => c.id === "YC31116")!;
+  assert.equal(openspec.team, null, "missing staff count stays unknown");
+  assert.equal(openspec.year, 2025, "a license copyright line alone is not a company founding date");
+  assert.equal(openspec.technicalReview!.level, "public-artifact");
+  assert.match(snapshot.companies.find((c) => c.id === "YC31095")!.businessModel!, /清洁/);
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR3-"));
+  assert.equal(figures.length, 8);
+  assert.ok(figures.every((o) => ["excluded", "adoption"].includes(o.selection) && !o.metric && o.comparisonGroup === null));
+  assert.equal(figures.find((o) => o.id.endsWith("SHORTKIT-OLD-ARR"))!.selection, "excluded");
+  assert.equal(figures.find((o) => o.id.endsWith("KALPA-TRAIN-COST"))!.selection, "excluded");
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR3-")));
+});
+
+test("the fourth uncertain cohort distinguishes product pivots, managed labor and pass-through money", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-4")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(72, 96);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 15);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 9);
+  assert.equal(batch.records.find((r) => r.name === "Orthogonal")!.companyId, "AI036");
+  assert.equal(snapshot.companies.filter((c) => c.url.endsWith("/companies/orthogonal")).length, 1);
+  const fern = snapshot.companies.find((c) => c.id === "YC31152")!;
+  assert.equal(fern.cat, "C02");
+  assert.match(fern.notes, /会议/);
+  const mocha = snapshot.companies.find((c) => c.id === "YC31269")!;
+  assert.equal(mocha.year, 2026);
+  assert.match(mocha.businessModel!, /人工/);
+  assert.match(mocha.price, /Platform.*Managed/);
+  assert.match(mocha.notes, /Coming soon/);
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR4-"));
+  assert.equal(figures.length, 7);
+  assert.ok(figures.every((o) => ["excluded", "adoption"].includes(o.selection) && !o.metric && o.comparisonGroup === null));
+  for (const suffix of ["LAURENCE-ADSPEND", "EKPA-CONNECTED-ASSETS", "REGBASE-CLIENT-GRANTS", "MOCHA-FUNDING"]) {
+    assert.equal(figures.find((o) => o.id.endsWith(suffix))!.selection, "excluded");
+  }
+  assert.equal(snapshot.companies.find((c) => c.id === "YC31199")!.technicalReview!.level, "public-artifact");
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR4-")));
+});
+
+test("the fifth uncertain cohort separates dated ARR, service run rates, bookings and previous ventures", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-5")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(96, 120);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 17);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 7);
+  assert.equal(batch.records.find((r) => r.name === "primitive")!.companyId, "AI002");
+  assert.equal(batch.records.find((r) => r.name === "Result")!.companyId, "AI001");
+  const result = snapshot.companies.find((c) => c.id === "AI001")!;
+  assert.equal(result.cat, "C09");
+  assert.equal(result.revenueStatus, "unknown");
+  assert.match(result.notes, /280万美元ARR属于用户/);
+  assert.ok(result.evidenceIds.includes("UNCLEAR5-31438-TERMS"));
+  const andco = snapshot.companies.find((c) => c.id === "YC31411")!;
+  assert.equal(andco.year, 2026, "a founder's previous venture must not replace the new company's stated year");
+  assert.match(andco.notes, /Layup/);
+  assert.equal(snapshot.evidence.find((e) => e.id === "UNCLEAR5-31411-LAUNCH6")!.companyId, null);
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR5-"));
+  assert.equal(figures.length, 18);
+  const dated = figures.find((o) => o.id.endsWith("TRELLIS-ARR-AUG"))!;
+  assert.equal(dated.metric, "arr");
+  assert.equal(dated.observedAt, undefined, "a page maintenance date is not a financial cutoff");
+  assert.ok(arrFieldGaps(dated, snapshot.asOf).includes("date"));
+  assert.equal(dated.bound, "≥");
+  assert.equal(figures.find((o) => o.id.endsWith("INSTA-RUNRATE"))!.metric, "annualized-revenue");
+  assert.equal(figures.find((o) => o.id.endsWith("INSTA-LAUNCH-ARR"))!.metric, "arr");
+  for (const suffix of ["PARROT-CONTRACT", "KINRO-OLD-CONTRACT", "BENTO-OLD-EMPLOYER", "TRELLIS-OLD-MGMT"]) {
+    assert.equal(figures.find((o) => o.id.endsWith(suffix))!.selection, "excluded");
+  }
+  assert.equal(snapshot.companies.find((c) => c.id === "YC31423")!.technicalReview!.level, "description",
+    "an internal benchmark subset is not an independent leaderboard result");
+  assert.ok(figures.every((o) => o.comparisonGroup === null));
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR5-")));
+});
+
+test("the sixth uncertain cohort preserves namesakes, churn and historical robotics MRR", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-6")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(120, 144);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 13);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 11);
+  const hera = snapshot.companies.find((c) => c.id === "YC32123")!;
+  const videoHera = snapshot.companies.find((c) => c.id === "AI025")!;
+  assert.equal(hera.cat, "C07");
+  assert.equal(hera.year, 2026);
+  assert.equal(videoHera.cat, "C08");
+  assert.equal(videoHera.year, 2025);
+  assert.notEqual(hera.url, videoHera.url, "same-name products with different YC pages must not be merged");
+  for (const name of ["Aernata", "Maquoketa Research", "Fire Wallet", "Eden Robotics", "Edgerun"]) {
+    const row = batch.records.find((r) => r.name === name)!;
+    assert.equal(row.status, "pending-ai");
+    assert.equal(row.companyId, undefined, "historical AI products, agent features and autonomous hardware need current-core evidence");
+  }
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR6-"));
+  assert.equal(figures.length, 13);
+  const churn = figures.find((o) => o.id.endsWith("ARDENT-CHURN"))!;
+  assert.equal(churn.selection, "excluded");
+  assert.equal(churn.metric, undefined, "lost ARR is not a positive current ARR observation");
+  assert.equal(snapshot.companies.find((c) => c.id === "YC31475")!.revenueStatus, "unknown");
+  const mrr = figures.find((o) => o.id.endsWith("AVEA-MRR"))!;
+  assert.equal(mrr.value, 18000);
+  assert.equal(mrr.metric, "mrr");
+  assert.equal(mrr.selection, "historical");
+  assert.match(mrr.unit, /无币种/);
+  assert.match(snapshot.companies.find((c) => c.id === "YC31530")!.notes, /Acquired/);
+  for (const suffix of ["PLAN0-PROJECTS-YC", "PLAN0-PROJECTS-LAUNCH", "SILMARIL-DAMAGE-YC", "SILMARIL-DAMAGE-CASE1", "SILMARIL-DAMAGE-CASE2", "EXPANSE-WASTE"]) {
+    assert.equal(figures.find((o) => o.id.endsWith(suffix))!.selection, "excluded");
+  }
+  const openwork = snapshot.companies.find((c) => c.id === "YC31505")!;
+  assert.equal(openwork.technicalReview!.level, "public-artifact");
+  assert.ok(openwork.technicalReview!.evidenceIds.includes("UNCLEAR6-31505-EE-LICENSE"));
+  assert.match(openwork.notes, /桌面MIT与ee目录/);
+  assert.ok(figures.every((o) => o.comparisonGroup === null));
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR6-")));
+});
+
+test("the seventh uncertain cohort separates hardware sales, prepayments and bounded verification", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-7")!;
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear").slice(144, 168);
+  assert.equal(batch.complete, false);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 16);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 8);
+  const robotNori = snapshot.companies.find((c) => c.id === "YC32553")!;
+  const healthNori = snapshot.companies.find((c) => c.id === "YC30657")!;
+  assert.equal(robotNori.cat, "C10");
+  assert.equal(healthNori.cat, "C09");
+  assert.notEqual(robotNori.url, healthNori.url, "same-name hardware and health companies retain distinct identities");
+  assert.equal(batch.records.find((r) => r.name === "Nori")!.companyId, robotNori.id);
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR7-"));
+  assert.equal(figures.length, 18);
+  const sales = figures.find((o) => o.id.endsWith("NORI-SALES"))!;
+  assert.equal(sales.value, 440000);
+  assert.equal(sales.selection, "historical");
+  assert.equal(sales.metric, undefined, "hardware sales with unverified preorder/delivery mix must not become ARR");
+  assert.match(snapshot.companies.find((c) => c.id === "YC32164")!.price!, /额度/);
+  for (const suffix of ["GUT-PHARMACY", "MANIFOLD-PICKS", "ATC-CLIENT-FLEET", "OPERON-CLIENT-SAVE", "ANYALPHA-OLD-APPS", "LETTER-DEMO"]) {
+    assert.equal(figures.find((o) => o.id.endsWith(suffix))!.selection, "excluded");
+  }
+  assert.match(figures.find((o) => o.id.endsWith("LETTER-DEMO"))!.scope, /fictional/);
+  assert.equal(snapshot.companies.find((c) => c.id === "YC34899")!.cat, "C06");
+  const kailash = snapshot.companies.find((c) => c.id === "YC34828")!;
+  assert.equal(kailash.cat, "C10");
+  assert.equal(kailash.technicalReview!.level, "description", "an open-weights claim without readable weights or a model card is not a reviewed public artifact");
+  const graphify = snapshot.companies.find((c) => c.id === "YC32583")!;
+  assert.equal(graphify.technicalReview!.level, "public-artifact");
+  const proof = snapshot.evidence.find((e) => e.id === "UNCLEAR7-32583-PROOF")!;
+  assert.match(proof.summary, /unknown/);
+  assert.match(proof.summary, /函数/);
+  assert.match(proof.limitation, /整库正确/);
+  assert.match(snapshot.companies.find((c) => c.id === "YC32518")!.notes, /Build标Coming soon/);
+  for (const name of ["Kara", "Molagri", "TareBio", "Atomarine", "Vernius Systems, Inc."]) {
+    const row = batch.records.find((r) => r.name === name)!;
+    assert.equal(row.status, "pending-ai");
+    assert.equal(row.companyId, undefined);
+  }
+  assert.ok(figures.every((o) => o.comparisonGroup === null));
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR7-")));
+});
+
+test("all original AI-unclear candidates retain a unique follow-up without counting EveryApp twice", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const rows = snapshot.candidateBatches!.filter((b) => b.id.startsWith("YC-AI-UNCLEAR-")).flatMap((b) => b.records);
+  const original = coverageAudit.records.filter((r) => r.status === "ai-unclear");
+  assert.equal(rows.length, 192);
+  assert.equal(new Set(rows.map((r) => r.url)).size, 192);
+  assert.deepEqual(rows.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(rows.filter((r) => r.status === "linked").length, 118);
+  assert.equal(rows.filter((r) => r.status === "pending-ai").length, 74);
+  assert.equal(snapshot.directoryRechecks!.filter((r) => r.ycId === 36088).length, 1);
+  const everyapp = rows.find((r) => r.name === "EveryApp")!;
+  assert.equal(everyapp.companyId, "AI074");
+  assert.equal(snapshot.companies.filter((c) => c.id === "AI074").length, 1);
+  for (const row of rows.filter((r) => r.status === "pending-ai")) {
+    assert.equal(row.companyId, undefined);
+    assert.ok(row.evidenceIds!.length > 0, "unresolved AI relevance has explicit evidence and gaps");
+  }
+  assert.equal(snapshot.coverage!.newProfiles, 928, "later evidence does not rewrite the original discovery accounting");
+});
+
+test("the final uncertain cohort separates delivery-start rentals, reservations and upstream costs", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-AI-UNCLEAR-8")!;
+  assert.equal(batch.complete, false, "reading the full pool does not resolve all AI, year or financial gaps");
+  assert.equal(batch.records.filter((r) => r.status === "linked").length, 11);
+  assert.equal(batch.records.filter((r) => r.status === "pending-ai").length, 13);
+  const rhem = snapshot.companies.find((c) => c.id === "YC35127")!;
+  assert.match(rhem.price!, /确认交付后/);
+  assert.match(rhem.price!, /99 USD/);
+  assert.equal(rhem.revenueStatus, "unknown");
+  assert.match(snapshot.companies.find((c) => c.id === "YC37272")!.price!, /预约/);
+  assert.match(snapshot.companies.find((c) => c.id === "AI074")!.price!, /有效订阅/);
+  assert.match(snapshot.companies.find((c) => c.id === "YC35396")!.notes, /Coming next/);
+  assert.match(snapshot.companies.find((c) => c.id === "YC36774")!.notes, /Early access/);
+  const figures = snapshot.observations.filter((o) => o.id.startsWith("UNCLEAR8-"));
+  assert.equal(figures.length, 9);
+  assert.ok(figures.every((o) => ["excluded", "adoption"].includes(o.selection) && !o.metric && o.comparisonGroup === null));
+  for (const suffix of ["OASIVE-OLD-WELLS", "OASIVE-OLD-GOOGLE", "OBSESSION-CALCULATOR", "EVERYAPP-UPSTREAM"]) {
+    assert.equal(figures.find((o) => o.id.endsWith(suffix))!.selection, "excluded");
+  }
+  for (const name of ["Clean", "STASIS", "RATLOG", "Inflection Space Systems", "twentyfour26"]) {
+    assert.equal(batch.records.find((r) => r.name === name)!.status, "pending-ai");
+  }
+  assert.ok(!platformIncomeSample(snapshot, { windowDays: null }).points.some((p) => p.observation.id.startsWith("UNCLEAR8-")));
+});
+
+test("founding conflicts preserve old product history without creating new companies or revenue", () => {
+  const snapshot = seed as ResearchSnapshot;
+  const batch = snapshot.candidateBatches!.find((b) => b.id === "YC-DATE-CONFLICT-1")!;
+  const original = coverageAudit.records.filter((r) => r.status === "date-conflict");
+  assert.equal(batch.complete, false);
+  assert.equal(batch.records.length, 6);
+  assert.deepEqual(batch.records.map((r) => r.url), original.map((r) => r.source));
+  assert.equal(new Set(batch.records.map((r) => r.url)).size, original.length);
+  for (const row of batch.records) {
+    assert.equal(row.status, "pending-year");
+    assert.equal(row.companyId, undefined);
+    const evidence = snapshot.evidence.filter((e) => row.evidenceIds!.includes(e.id));
+    assert.equal(evidence.length, row.evidenceIds!.length);
+    assert.ok(evidence.every((e) => e.companyId === null));
+    assert.ok(evidence.some((e) => e.publishedAt?.startsWith("2025-")), "old product launch remains explicit");
+    assert.ok(!snapshot.companies.some((c) => c.evidenceIds.some((eid) => row.evidenceIds!.includes(eid))));
+  }
+  assert.match(batch.records.find((r) => r.name === "Layers")!.note, /未确认当前AI核心/);
+  assert.ok(!snapshot.observations.some((o) => o.evidence.startsWith("YEARCONFLICT1-")));
+  const excluded = coverageAudit.records.filter((r) => r.status === "year-outside-or-missing");
+  assert.equal(excluded.filter((r) => r.foundedYear === null).length, 54);
+  assert.equal(excluded.filter((r) => r.foundedYear !== null).length, 214);
+  assert.ok(excluded.filter((r) => r.foundedYear !== null).every((r) => ![2025, 2026].includes(r.foundedYear!)));
+});
+
 test("Chinese discovery entries preserve unread articles and use separate primary evidence", () => {
   const snapshot = structuredClone(seed) as ResearchSnapshot;
   const batch = snapshot.candidateBatches!.find((b) => b.id === "JQ-2026-targeted-1")!;

content/research/ai-income-analysis.json

@@ -1,6 +1,6 @@
 {
-  "sourceVersion": "2026-10-30-battlefield-3",
-  "asOf": "2026-10-30",
+  "sourceVersion": "2026-11-01-sr007-followup-10",
+  "asOf": "2026-11-01",
   "metric": "platform-mrr-usd",
   "sampleUnit": "product-or-business",
   "note": "独立Python复算参考值，仅验证样本计算；不是行业估计或财务审计。",
@@ -21,7 +21,9 @@
         "MR011",
         "MR012",
         "MR015",
-        "MR016"
+        "MR016",
+        "MR018",
+        "MR019"
       ],
       "groups": [
         {
@@ -35,7 +37,7 @@
             "MR016"
           ],
           "n": 4,
-          "mean": 34815.0,
+          "mean": 34815,
           "median": 36723.0,
           "sampleDeviation": 16334.54845820151,
           "cv": 0.46918134304758036,
@@ -50,7 +52,7 @@
             "MR001"
           ],
           "n": 1,
-          "mean": 53101.0,
+          "mean": 53101,
           "median": 53101,
           "sampleDeviation": null,
           "cv": null,
@@ -65,7 +67,7 @@
             "MR002"
           ],
           "n": 1,
-          "mean": 62423.0,
+          "mean": 62423,
           "median": 62423,
           "sampleDeviation": null,
           "cv": null,
@@ -79,15 +81,17 @@
           "products": [
             "MR003",
             "MR004",
-            "MR005"
+            "MR005",
+            "MR018",
+            "MR019"
           ],
-          "n": 3,
-          "mean": 41693.0,
-          "median": 49069,
-          "sampleDeviation": 28111.375064197768,
-          "cv": 0.6742468775141575,
-          "meanWithoutLargest": 29849.0,
-          "largestRemovalDrop": 0.2840764636749574
+          "n": 5,
+          "mean": 51674.8,
+          "median": 65381,
+          "sampleDeviation": 24138.82269705795,
+          "cv": 0.4671294847209461,
+          "meanWithoutLargest": 47627.5,
+          "largestRemovalDrop": 0.07832250923080501
         },
         {
           "category": "C09",
@@ -105,7 +109,7 @@
           "median": 20483,
           "sampleDeviation": 11942.947278624319,
           "cv": 0.4711370488466823,
-          "meanWithoutLargest": 20046.0,
+          "meanWithoutLargest": 20046,
           "largestRemovalDrop": 0.20920581320120557
         }
       ]
@@ -127,9 +131,27 @@
         "MR012",
         "MR015",
         "MR016",
-        "AI016"
+        "AI016",
+        "MR017",
+        "MR018",
+        "MR019"
       ],
       "groups": [
+        {
+          "category": "C02",
+          "provider": "Stripe",
+          "model": "模型网关订阅与用量",
+          "products": [
+            "MR017"
+          ],
+          "n": 1,
+          "mean": 78764,
+          "median": 78764,
+          "sampleDeviation": null,
+          "cv": null,
+          "meanWithoutLargest": null,
+          "largestRemovalDrop": null
+        },
         {
           "category": "C06",
           "provider": "Stripe",
@@ -146,7 +168,7 @@
           "median": 40112,
           "sampleDeviation": 174786.78744916618,
           "cv": 1.5505509613536426,
-          "meanWithoutLargest": 34815.0,
+          "meanWithoutLargest": 34815,
           "largestRemovalDrop": 0.6911526751687284
         },
         {
@@ -157,7 +179,7 @@
             "MR001"
           ],
           "n": 1,
-          "mean": 53101.0,
+          "mean": 53101,
           "median": 53101,
           "sampleDeviation": null,
           "cv": null,
@@ -172,7 +194,7 @@
             "MR002"
           ],
           "n": 1,
-          "mean": 62423.0,
+          "mean": 62423,
           "median": 62423,
           "sampleDeviation": null,
           "cv": null,
@@ -186,15 +208,17 @@
           "products": [
             "MR003",
             "MR004",
-            "MR005"
+            "MR005",
+            "MR018",
+            "MR019"
           ],
-          "n": 3,
-          "mean": 41693.0,
-          "median": 49069,
-          "sampleDeviation": 28111.375064197768,
-          "cv": 0.6742468775141575,
-          "meanWithoutLargest": 29849.0,
-          "largestRemovalDrop": 0.2840764636749574
+          "n": 5,
+          "mean": 51674.8,
+          "median": 65381,
+          "sampleDeviation": 24138.82269705795,
+          "cv": 0.4671294847209461,
+          "meanWithoutLargest": 47627.5,
+          "largestRemovalDrop": 0.07832250923080501
         },
         {
           "category": "C09",
@@ -212,7 +236,7 @@
           "median": 20483,
           "sampleDeviation": 11942.947278624319,
           "cv": 0.4711370488466823,
-          "meanWithoutLargest": 20046.0,
+          "meanWithoutLargest": 20046,
           "largestRemovalDrop": 0.20920581320120557
         }
       ]

scripts/audit-ai-research.py

@@ -16,6 +16,20 @@ SOURCE_SLUG = 'ai-company-research/02-coverage-and-gaps'
 BASE = '/docs/other-research/ai-company-research/overview'
 
 
+def yc_coverage_summary():
+    companies = {row['id']: row for row in DATA['companies']}
+    imported = [companies[f"YC{row['ycId']}"] for row in AUDIT['records'] if row['status'] == 'imported']
+    profile_only = sum(bool(row.get('profileOnly')) for row in imported)
+    groups = {}
+    for label, prefix in [('unclearAI', 'YC-AI-UNCLEAR-'), ('missingYear', 'YC-MISSING-YEAR-'),
+                          ('dateConflict', 'YC-DATE-CONFLICT-')]:
+        rows = [row for batch in DATA.get('candidateBatches', []) if batch['id'].startswith(prefix)
+                for row in batch['records']]
+        groups[label] = {'records': len(rows), 'statuses': dict(sorted(Counter(row['status'] for row in rows).items()))}
+    return {'initialProfiles': len(AUDIT['records']), 'originalImported': len(imported),
+            'profileOnly': profile_only, 'deepenedOriginal': len(imported) - profile_only, **groups}
+
+
 
 def source_register():
     sources = SOURCES['sources']
@@ -26,6 +40,15 @@ def source_register():
     for source in sources:
         assert source['status'] in {'candidate-audit', 'targeted', 'pending', 'context', 'access-limited', 'per-company'}
         assert source['boundary'] and source['nextCheck']
+        if source['id'] == 'S01':
+            assert source.get('coverageSummary') == yc_coverage_summary(), 'YC source summary is stale'
+        if source['id'] == 'S14' and source.get('rosterAudit'):
+            roster = source['rosterAudit']
+            batch = next(batch for batch in DATA['candidateBatches'] if batch['id'] == roster['candidateBatchId'])
+            assert batch['sourceId'] == 'S14' and not batch['complete']
+            assert roster['defaultCohort'] == 'SR007' and roster['next'] is None
+            assert len(batch['records']) == roster['recordCount'] == roster['reportedCount']
+            assert len({row['url'] for row in batch['records']}) == roster['recordCount']
         if source['url']:
             assert urlsplit(source['url']).scheme in {'http', 'https'} and urlsplit(source['url']).netloc
         for host in source['hosts']:
@@ -49,7 +72,18 @@ def source_register():
         company_ids = {e['companyId'] for e in evidence if e['companyId']}
         observations = sum(o['evidence'] in evidence_ids for o in DATA['observations'])
         name = f"[{source['name']}]({source['url']})" if source['url'] else source['name']
-        lines.append(f"| {source['id']} {name} | {statuses[source['status']]} | {len(evidence)} / {len(company_ids)} / {observations} | {source['boundary']} | {source['nextCheck']} |")
+        boundary = source['boundary']
+        if source['id'] == 'S01':
+            summary = source['coverageSummary']
+            boundary += (f" 当前原候选池{summary['initialProfiles']}项，原新增{summary['originalImported']}项中"
+                         f"{summary['profileOnly']}项目录基础资料、{summary['deepenedOriginal']}项深化个案。")
+            for key, label in [('unclearAI', '原AI不明'), ('missingYear', '原缺年'), ('dateConflict', '原日期冲突')]:
+                group = summary[key]
+                labels = {'linked': '关联公司', 'pending-ai': 'AI主营待核',
+                          'pending-year': '成立/主体待核', 'outside-cohort': '范围外'}
+                counts = '、'.join(f'{labels.get(status, status)} {count}' for status, count in group['statuses'].items())
+                boundary += f" {label}{group['records']}项当前：{counts}。"
+        lines.append(f"| {source['id']} {name} | {statuses[source['status']]} | {len(evidence)} / {len(company_ids)} / {observations} | {boundary} | {source['nextCheck']} |")
     lines += ['', 'Dealroom、Product Hunt、机器之心与36氪入口于2026-10-30重新访问。官网首页可读不证明已取得完整筛选结果；安全检测页面不作为文章内容。候选池之外的来源边界尚需建立可回查条目，完成前不宣布来源清单已全部覆盖。', '']
     return lines
 
@@ -85,7 +119,7 @@ def candidate_register():
     all_ids = [r['id'] for b in batches for r in b['records']]
     assert len(set(all_ids)) == len(all_ids)
     lines = []
-    statuses = {'pending-year': '成立年待核', 'linked': '已关联公司',
+    statuses = {'pending-year': '成立年待核', 'pending-ai': 'AI主营待核', 'linked': '已关联公司',
                 'existing-outside-cohort': '已有关联，年份范围外',
                 'outside-cohort': '范围外或既有业务，不新增公司', 'promotion': '推广活动，非公司新增'}
     for batch in batches:
@@ -97,13 +131,13 @@ def candidate_register():
         assert sum(counts.values()) == len(rows)
         lines += ['', f"## {batch['sourceId']} 候选核对（{batch['id']}）", '',
             f"[发现页面]({batch['url']})，读取于{batch['accessedAt']}。{batch['scope']}", '',
-            f"本批{len(rows)}条：{counts['linked']}条关联公司、{counts['existing-outside-cohort']}条关联范围外对照、{counts['outside-cohort']}条范围外不新增公司、{counts['pending-year']}条成立年待核、{counts['promotion']}条推广活动。完整来源覆盖标记：{'已完成' if batch['complete'] else '未完成'}。主营或产品任务的初分类不计入主公司表数量，只有已关联的公司记录进入对应公司明细。候选与缺项随研究版本保存在文档站同一SQLite的研究元数据中。", '',
+            f"本批{len(rows)}条：{counts['linked']}条关联公司、{counts['existing-outside-cohort']}条关联范围外对照、{counts['outside-cohort']}条范围外不新增公司、{counts['pending-year']}条成立年待核、{counts['pending-ai']}条AI主营待核、{counts['promotion']}条推广活动。完整来源覆盖标记：{'已完成' if batch['complete'] else '未完成'}。主营或产品任务的初分类不计入主公司表数量，只有已关联的公司记录进入对应公司明细。候选与缺项随研究版本保存在文档站同一SQLite的研究元数据中。", '',
             '| 发现条目 | 主营或产品任务初分 | 核对状态 | 关联公司 | 核对证据 | 限制 |', '| --- | --- | --- | --- | --- | --- |']
         for row in rows:
             assert row['categoryId'] in categories
             assert row['status'] in statuses
             assert all(eid in evidence for eid in row.get('evidenceIds', []))
-            if row['status'] == 'outside-cohort':
+            if row['status'] in {'outside-cohort', 'pending-ai'}:
                 assert row.get('evidenceIds') and not row.get('companyId')
             name = f"[{row['name']}]({row['url']})" if row['url'] else row['name']
             company = '—'
@@ -121,6 +155,73 @@ def candidate_register():
     return lines
 
 
+def uncertain_ai_followups():
+    original = {row['source']: row for row in AUDIT['records'] if row['status'] == 'ai-unclear'}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-AI-UNCLEAR-') for row in batch['records']]
+    if not rows:
+        return []
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls), 'Duplicate AI-relevance follow-up URL'
+    rechecks = {row['ycId']: row for row in DATA.get('directoryRechecks', [])}
+    counts = Counter(row['status'] for row in rows)
+    assert set(counts) <= {'linked', 'pending-ai'}
+    for row in rows:
+        assert row['url'] in original and row.get('evidenceIds')
+        baseline = original[row['url']]
+        if row['status'] == 'linked':
+            assert rechecks[baseline['ycId']]['companyId'] == row['companyId']
+        else:
+            assert not row.get('companyId') and baseline['ycId'] not in rechecks
+    unread = len(original) - len(rows)
+    assert unread >= 0
+    return ['', '## 原AI主营不明名单的后续覆盖', '',
+        f"原名单{len(original)}项，已有后续记录{len(rows)}项：{counts['linked']}项关联公司、{counts['pending-ai']}项仍待核AI主营，{unread}项尚无本批系列的后续记录。计数按原始YC页面去重；EveryApp此前的独立复核与末批记录不重复相加。", '',
+        '读完名单只证明每项有可回查的处理记录，不证明待核项均为AI公司，也不证明法律主体、收入或技术效果已独立验证。原始发现审计保持不变，成立年范围外/缺失、成立冲突及其他来源的缺项仍单列。', '']
+
+
+def founding_year_followups():
+    original = {row['source']: row for row in AUDIT['records'] if row['status'] == 'date-conflict'}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-DATE-CONFLICT-') for row in batch['records']]
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls), 'Duplicate founding-conflict follow-up URL'
+    assert set(urls) <= set(original), 'Unknown founding-conflict follow-up URL'
+    evidence = {row['id']: row for row in DATA['evidence']}
+    for row in rows:
+        assert row['status'] == 'pending-year' and not row.get('companyId')
+        assert row.get('evidenceIds') and all(eid in evidence for eid in row['evidenceIds'])
+        assert all(evidence[eid]['companyId'] is None for eid in row['evidenceIds'])
+    unread = len(original) - len(rows)
+    return ['', '## 原成立年冲突名单的后续覆盖', '',
+        f"原名单{len(original)}项，已有后续记录{len(rows)}项，均保留成立年待核，{unread}项尚无本系列后续记录。当前业务初分类见候选表，不计入主公司表及收入统计。", '',
+        '2025旧品牌发布可证明当时已有产品材料；当前目录的2026字段、批次、条款日期和运营者名称不能单独证明法律主体成立或新旧主体连续。未据此强改成立年。Layers当前AI核心仍另待核。', '']
+
+
+def missing_year_followups():
+    original = {row['source'] for row in AUDIT['records']
+                if row['status'] == 'year-outside-or-missing' and row['foundedYear'] is None}
+    rows = [row for batch in DATA.get('candidateBatches', [])
+            if batch['id'].startswith('YC-MISSING-YEAR-') for row in batch['records']]
+    urls = [row['url'] for row in rows]
+    assert len(set(urls)) == len(urls) and set(urls) <= original
+    evidence = {e['id']: e for e in DATA['evidence']}
+    for row in rows:
+        assert row['status'] in {'outside-cohort', 'pending-year', 'pending-ai', 'linked'}
+        assert row.get('evidenceIds')
+        if row['status'] == 'linked':
+            matches = [r for r in DATA.get('directoryRechecks', []) if r['companyId'] == row.get('companyId')]
+            assert len(matches) == 1 and matches[0]['originalStatus'] == 'year-outside-or-missing'
+            assert all(eid in evidence and evidence[eid]['companyId'] == row['companyId'] for eid in row['evidenceIds'])
+        else:
+            assert not row.get('companyId')
+            assert all(eid in evidence and evidence[eid]['companyId'] is None for eid in row['evidenceIds'])
+    counts = Counter(row['status'] for row in rows)
+    return ['', '## 原缺少成立年名单的后续覆盖', '',
+        f"原快照缺年{len(original)}项，外部补核已有{len(rows)}项：{counts['linked']}项关联公司、{counts['outside-cohort']}项既有主体/业务范围外、{counts['pending-year']}项成立年待核、{counts['pending-ai']}项AI主营待核，{len(original)-len(rows)}项尚无本系列外部补核记录。", '',
+        '当前YC缺年字段复读与外部主体核查分别统计。登记创建日、公司自述年、产品发布、条款更新和批次不相互替代；已查英国或法国主体不代表完整集团沿革已核。原54项发现计数不回写，也不以Inactive补零收入。', '']
+
+
 def directory_rechecks():
     rows = DATA.get('directoryRechecks', [])
     if not rows:
@@ -167,6 +268,11 @@ def render():
         'year-outside-or-missing': cov['otherOrMissingYear'],
         'ai-unclear': cov['unclearAI'], 'date-conflict': cov['dateConflicts']}
     assert len({x['ycId'] for x in AUDIT['records']}) == len(AUDIT['records'])
+    excluded_years = Counter(x['foundedYear'] for x in AUDIT['records'] if x['status'] == 'year-outside-or-missing')
+    outside_years = {year: count for year, count in excluded_years.items() if year is not None}
+    assert all(year not in (2025, 2026) for year in outside_years)
+    assert sum(outside_years.values()) + excluded_years[None] == cov['otherOrMissingYear']
+    year_detail = '、'.join(f'{year}年{count}项' for year, count in sorted(outside_years.items()))
     lines = ['# AI 公司资料覆盖与缺项', '',
         f"截至 {DATA['asOf']}，研究表含{len(companies):,}条公司或产品记录。YC候选池有逐条读取审计；其他来源为定向补充。清单尚不能作为全球AI公司普查，也不能将收入未知解释为零。", '',
         '验收范围已于2026-10-30确认为：以确认的公开来源清单为边界，逐条核对并标明缺项。下方区分已逐条审计的候选池与定向补充来源；已使用某来源不代表该来源已被完整覆盖。', '',
@@ -178,7 +284,8 @@ def render():
         f"| 初筛匹配 | {cov['matchingProfiles']:,} | 成立年及AI相关描述通过初筛，尚非逐家商业验证 |",
         f"| 与原表重合 | {cov['existingProfiles']} | URL匹配，未再次新增 |",
         f"| 新增目录记录 | {cov['newProfiles']:,} | 原始入表来源，后续深化不改变发现计数 |",
-        f"| 年份不符或缺失 | {cov['otherOrMissingYear']} | 此轮不作为成立年匹配项 |",
+        f"| 原目录列为范围外年份 | {sum(outside_years.values())} | {year_detail}；原快照自述，不是独立登记核验 |",
+        f"| 原目录未给成立年 | {excluded_years[None]} | 与已列范围外年份分开，仍待补证 |",
         f"| AI业务待确认 | {cov['unclearAI']} | 不强行把背景关键词当主营 |",
         f"| 成立年与批次冲突 | {cov['dateConflicts']} | 另保留核查记录 |", '',
         f"全表由{depth}条目录基础项和{len(companies)-depth}条个案或平台记录组成。候选池已匹配项之外另有{len(companies)-cov['matchingProfiles']}条记录，包含其他来源及发布/转型对照；其中已有{promoted}条原目录记录深化为个案，仍保留目录来源。两种统计边界不同。候选池数字只是该镜像快照的覆盖，Bud等后续定向补充不回填为原候选池已覆盖。", '',
@@ -191,8 +298,8 @@ def render():
         f"| 其他目录基础项 | {depth-reviewed} | 规则初分或资料不足，继续待复核 |",
         f"| 有团队数字 | {sum(c['team'] is not None for c in companies)} | 多为自报Team Size或Employees，时点与口径不统一 |",
         f"| 专项技术证据记录 | {len(companies)-technical['unreviewed']} | 其中{technical['description']}条自述、{technical['public-artifact']}条公开材料、{technical['independent-test']}条第三方评测 |",
-        f"| 公司披露收入线索 | {income['self']} | 含历史、下界、币种不明及产品业务声明，不等于可比ARR |",
-        f"| 仅平台收入线索 | {income['platform']} | 同时有公司披露者计入上一行，不重复计数 |",
+        f"| 企业披露收入线索 | {income['self']} | 含公司或交易当事方披露、历史、下界及币种不明，不等于可比ARR |",
+        f"| 仅平台收入线索 | {income['platform']} | 同时有企业披露者计入上一行，不重复计数 |",
         f"| 仅第三方估算 | {income['estimate']} | 不与直接披露混合 |",
         f"| 暂无可用收入数字 | {income['unknown']} | 包含尚未逐家搜索的目录项，不代表没有收入 |", '',
         '## 业务分类与数据入口', '',
@@ -217,13 +324,16 @@ def render():
         lines.append(f"| {name} | {len(entries)} | {len({e['url'] for e in entries})} | {note} |")
     others = [e for e in DATA['evidence'] if urlsplit(e['url']).netloc not in included]
     lines.append(f"| 其他官网、评测及公开资料 | {len(others)} | {len({e['url'] for e in others})} | 按每条来源限制理解，不能统一当作独立证据 |")
+    lines += uncertain_ai_followups()
+    lines += founding_year_followups()
+    lines += missing_year_followups()
     lines += directory_rechecks()
     lines += source_register()
     lines += context_methods()
     lines += candidate_register()
     lines += ['', '## 尚未满足的研究条件', '',
         '行业ARR比较仍缺统一业务截止日、币种、经常性定义和业务边界。现有平台MRR可描述已采集产品，不足以代表全部新公司。技术评测只覆盖所列版本与任务，不宜跨领域汇总为统一强弱分数。', '',
-        '下一轮优先补收入线索的日期及合同定义、非YC和非英文来源、规则初分的人工复核，以及停止服务或被收购案例。这样能减少“只见赢家”和“只见融资公司”的偏差。公开来源池之外的未披露公司仍无法证明已被穷尽。', '',
+        '下一轮优先补收入线索的日期及合同定义、成立年冲突、AI主营仍待核项、非YC和非英文来源，以及停止服务或被收购案例。这样能减少“只见赢家”和“只见融资公司”的偏差。公开来源池之外的未披露公司仍无法证明已被穷尽。', '',
     ]
     return '\n'.join(lines)
 

scripts/summarize-ai-income.py

@@ -89,23 +89,42 @@ def link(cid):
 
 def render():
     points, excluded = sample(7)
+    grouped = groups(points)
+    described = [(key, stats(values)) for key, values in sorted(grouped.items())]
+    sizes = [value['n'] for _, value in described]
+    eligible = [(key, value) for key, value in described if value['n'] >= 3]
+    interpretation = '各组均不足3个样本，暂不比较分组离散度或收入高低。'
+    if eligible:
+        highest_key, highest = max(eligible, key=lambda item: item[1]['mean'])
+        sensitive_key, sensitive = max(eligible, key=lambda item: item[1]['largestRemovalDrop'] or 0)
+        cvs = [value['cv'] for _, value in eligible if value['cv'] is not None]
+        cv_text = f"CV范围为{min(cvs):.3f}—{max(cvs):.3f}。" if cvs else '各组CV均不可计算。'
+        interpretation = (
+            f"在至少3个样本的组内，当前均值最高的是{CATEGORIES[highest_key[0]]} · {highest_key[2]}"
+            f"（{highest_key[1]}，{money(highest['mean'])}美元每月）。{cv_text}"
+            f"剔除最大值后均值下降最多的是{CATEGORIES[sensitive_key[0]]} · {sensitive_key[2]}"
+            f"（{sensitive_key[1]}，下降{sensitive['largestRemovalDrop'] or 0:.1%}）。"
+            '这些只是当前采集点的描述，不据微小CV差异宣称某领域更稳定。'
+        )
+    size_text = f"各组目前只有{min(sizes)}至{max(sizes)}个产品" if sizes else '当前没有可比较的产品'
     lines = [
         '# AI 公司收入样本分布快照', '',
-        f"截至 {DATA['asOf']}，7日窗口内有 {len(points)} 个产品的美元平台MRR可纳入分组描述。创意软件订阅组的样本均值较高、CV也较大；营销软件组与消费者个人应用组的CV接近。各组只有1至5个产品，尚不足以估计行业总体或造假概率。", '',
+        f"截至 {DATA['asOf']}，7日窗口内有 {len(points)} 个产品的美元平台MRR可纳入分组描述。按业务、支付平台和收费模式分别计算均值、中位数与离散度。{size_text}，尚不足以估计行业总体或造假概率。", '',
         f"这是已采集资料的固定快照。[动态工作台]({BASE})提供筛选与原始证据。窗口按最后同步日计算，不保证所有点同日，也不是{len(DATA['companies']):,}条公司/产品记录的收入普查。", '',
         '## 7日窗口的分组结果', '',
         'MRR单位为美元每月。CV为样本标准差除以均值；最大值剔除降幅表示移除最高收入产品后均值下降的比例。单个样本不计算离散度。', '',
         '| 业务及收费模式 | 平台 | 样本数 | 均值 | 中位数 | CV | 最大值剔除降幅 |',
         '| --- | --- | --- | --- | --- | --- | --- |',
     ]
-    for key, values in sorted(groups(points).items()):
+    for key, values in sorted(grouped.items()):
         cat, provider, model = key
         v = stats(values)
         cv = f"{v['cv']:.3f}" if v['cv'] is not None else '不计算'
         drop = f"{v['largestRemovalDrop']:.1%}" if v['largestRemovalDrop'] is not None else '不计算'
         lines.append(f"| {CATEGORIES[cat]} · {model} | {provider} | {v['n']} | {money(v['mean'])} | {money(v['median'])} | {cv} | {drop} |")
     lines += ['',
-        '在至少3个样本的组内，内容创意软件订阅的均值较高，但移除最大样本后的下降也最大。营销软件与个人应用的CV仅有微小差别，不据此宣称哪个领域更稳定。两个内容类单样本组虽有更高数值，不能作为典型收入或领域排名。', '',
+        interpretation, '',
+        '单个样本不计算离散度，也不能作为典型收入或领域排名；不同收费模式和支付平台的分组保留各自含义。', '',
         '均值和中位数接近只描述当前采集点，并不证明样本具有代表性。平台公开者、成立年、规模、获客渠道与收费方式均可能影响结果。', '',
         '## 样本与证据入口', '',
         '点击产品名称可打开公司明细，再查看平台记录及其限制。连接已过期的近期快照仍按所标日期保留，并不表示连接当前有效。', '',